From 5170369585f6582c4083b59f03460e4ec5a58926 Mon Sep 17 00:00:00 2001 From: HelloWorldLTY Date: Sun, 8 Dec 2024 10:29:42 -0500 Subject: [PATCH 01/16] update cellplm and scfoundation --- src/methods/cellplm/config.vsh.yaml | 74 + src/methods/cellplm/script.py | 92 + src/methods/scfoundation/config.vsh.yaml | 78 + src/methods/scfoundation/script.py | 70 + src/utils/OS_scRNA_gene_index.19264.tsv | 19265 ++++++++++++++++ src/utils/get_embedding.py | 259 + src/utils/load.py | 198 + src/utils/pretrainmodels/__init__.py | 3 + src/utils/pretrainmodels/mae_autobin.py | 162 + src/utils/pretrainmodels/performer.py | 638 + .../pretrainmodels/pytorchTransformer.py | 80 + src/utils/pretrainmodels/reversible.py | 200 + src/utils/pretrainmodels/select_model.py | 61 + src/utils/pretrainmodels/transformer.py | 43 + 14 files changed, 21223 insertions(+) create mode 100755 src/methods/cellplm/config.vsh.yaml create mode 100644 src/methods/cellplm/script.py create mode 100644 src/methods/scfoundation/config.vsh.yaml create mode 100644 src/methods/scfoundation/script.py create mode 100644 src/utils/OS_scRNA_gene_index.19264.tsv create mode 100644 src/utils/get_embedding.py create mode 100644 src/utils/load.py create mode 100644 src/utils/pretrainmodels/__init__.py create mode 100644 src/utils/pretrainmodels/mae_autobin.py create mode 100644 src/utils/pretrainmodels/performer.py create mode 100644 src/utils/pretrainmodels/pytorchTransformer.py create mode 100644 src/utils/pretrainmodels/reversible.py create mode 100644 src/utils/pretrainmodels/select_model.py create mode 100644 src/utils/pretrainmodels/transformer.py diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml new file mode 100755 index 00000000..54390a1a --- /dev/null +++ b/src/methods/cellplm/config.vsh.yaml @@ -0,0 +1,74 @@ +# The API specifies which type of component this is. +# It contains specifications for: +# - The input/output files +# - Common parameters +# - A unit test +__merge__: ../../api/comp_method.yaml + +# A unique identifier for your component (required). +# Can contain only lowercase letters or underscores. +name: cellplm +# A relatively short label, used when rendering visualisations (required) +label: cellplm +# A one sentence summary of how this method works (required). Used when +# rendering summary tables. +summary: "A foundation model pre-trained with cells as tokens." +# A multi-line description of how this component works (required). Used +# when rendering reference documentation. +description: | + CellPLM is a pre-trained language model specifically designed for single-cell analysis that leverages the principles of natural language processing (NLP) to understand and process single-cell gene expression data. +references: + doi: + - https://openreview.net/forum?id=BKXvPDekud +# bibtex: +# - | +# @article{foo, +# title={Foo}, +# author={Bar}, +# journal={Baz}, +# year={2024} +# } +links: + # URL to the documentation for this method (required). + documentation: https://github.com/OmicsML/CellPLM + # URL to the code repository for this method (required). + repository: https://github.com/OmicsML/CellPLM + + +info: + method_types: [embedding] + preferred_normalization: counts + +# arguments: +# - name: --model +# type: string +# description: String giving the scGPT model to use +# choices: ["cellplm"] +# default: "cellplm" +# - name: --n_hvg +# type: integer +# default: 3000 +# description: Number of highly variable genes to use. + +resources: + - type: python_script + path: script.py + - path: /src/utils/read_anndata_partial.py + +engines: + - type: docker + image: openproblems/base_pytorch_nvidia:1.0.0 + # TODO: Try to find working installation of flash attention (flash-attn<1.0.5) + setup: + - type: python + pypi: + - gdown + - scgpt # Install from PyPI to get dependencies + - cellplm + - scanpy + +runners: + - type: executable + - type: nextflow + directives: + label: [midtime, midmem, midcpu, gpu] diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py new file mode 100644 index 00000000..d9e8d3d9 --- /dev/null +++ b/src/methods/cellplm/script.py @@ -0,0 +1,92 @@ +import sys +import tempfile +import scanpy as sc +import anndata as ad +import gdown +import cellplm +import torch + +import warnings +warnings.filterwarnings("ignore") +from CellPLM.utils import set_seed + +import numpy as np +import anndata as ad +from CellPLM.pipeline.cell_embedding import CellEmbeddingPipeline + +## VIASH START +# Note: this section is auto-generated by viash at runtime. To edit it, make changes +# in config.vsh.yaml and then run `viash config inject config.vsh.yaml`. + +drive_path = f"https://drive.google.com/drive/folders/1C2fVNEKX3plHnagaTwpuPW5tpwv1up9G?usp=sharing" +model_dir = tempfile.TemporaryDirectory() +print(f"Downloading from '{drive_path}'", flush=True) +gdown.download_folder(drive_path, output=model_dir.name, quiet=True) +print(f"Model directory: '{model_dir.name}'", flush=True) + +set_seed(24) +PRETRAIN_VERSION = '20231027_85M' +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" + +par = { + 'input': 'resources_test/.../input.h5ad', + 'output': 'output.h5ad' +} +meta = { + 'name': 'cellplm' +} +## VIASH END +print(f"====== scGPT version {PRETRAIN_VERSION} ======", flush=True) + +sys.path.append(meta["resources_dir"]) +from read_anndata_partial import read_anndata + +print("\n>>> Reading input files...", flush=True) +print(f"Input H5AD file: '{par['input']}'", flush=True) +adata = read_anndata(par["input"], X="layers/counts", obs="obs", var="var", uns="uns") + +if adata.uns["dataset_organism"] != "homo_sapiens": + raise ValueError( + f"CellPLM can only be used with human data " + f"(dataset_organism == \"{adata.uns['dataset_organism']}\")" + ) + +print(adata, flush=True) + +print('Preprocess data', flush=True) +# ... preprocessing ... +sc.pp.normalize_total(adata) +sc.pp.log1p(adata) + +print('Train model', flush=True) +# ... train model ... + +pipeline = CellEmbeddingPipeline(pretrain_prefix=PRETRAIN_VERSION, # Specify the pretrain checkpoint to load + pretrain_directory=model_dir) + +embedding = pipeline.predict(adata, # An AnnData object + device=DEVICE) # Specify a gpu or cpu for model inference + +embedding = embedding.cpu().numpy() + +print('Generate predictions', flush=True) +# ... generate predictions ... + +output = ad.AnnData( + obs=adata.obs[[]], + var=adata.var[[]], + obsm={ + "X_emb": embedding, + }, + uns={ + "dataset_id": adata.uns["dataset_id"], + "normalization_id": adata.uns["normalization_id"], + "method_id": meta["name"], + }, +) +print(output) + +output.write_h5ad(par['output'], compression='gzip') + +print("\n>>> Cleaning up temporary directories...", flush=True) +model_dir.cleanup() diff --git a/src/methods/scfoundation/config.vsh.yaml b/src/methods/scfoundation/config.vsh.yaml new file mode 100644 index 00000000..0d6279f9 --- /dev/null +++ b/src/methods/scfoundation/config.vsh.yaml @@ -0,0 +1,78 @@ +# The API specifies which type of component this is. +# It contains specifications for: +# - The input/output files +# - Common parameters +# - A unit test +__merge__: ../../api/comp_method.yaml + +# A unique identifier for your component (required). +# Can contain only lowercase letters or underscores. +name: scfoundation +# A relatively short label, used when rendering visualisations (required) +label: scfoundation +# A one sentence summary of how this method works (required). Used when +# rendering summary tables. +summary: "A foundation model pre-trained with downsampling pre-training method." +# A multi-line description of how this component works (required). Used +# when rendering reference documentation. +description: | + scFoundation is a large-scale foundation model designed specifically for single-cell genomics analysis. It leverages self-supervised learning strategies to understand the complex patterns in single-cell gene expression data, enabling various downstream tasks through transfer learning and fine-tuning approaches. +references: + doi: + - https://www.nature.com/articles/s41592-024-02305-7 +# bibtex: +# - | +# @article{foo, +# title={Foo}, +# author={Bar}, +# journal={Baz}, +# year={2024} +# } +links: + # URL to the documentation for this method (required). + documentation: https://github.com/biomap-research/scFoundation + # URL to the code repository for this method (required). + repository: https://github.com/biomap-research/scFoundation + + + +info: + method_types: [embedding] + preferred_normalization: counts + +# arguments: +# - name: --model +# type: string +# description: String giving the scGPT model to use +# choices: ["cellplm"] +# default: "cellplm" +# - name: --n_hvg +# type: integer +# default: 3000 +# description: Number of highly variable genes to use. + +resources: + - type: python_script + path: script.py + - path: /src/utils/read_anndata_partial.py + - path: /src/utils/load.py + - path: /src/utils/get_embedding.py + + - type: file + path: /src/utils/OS_scRNA_gene_index.19264.tsv + +engines: + - type: docker + image: openproblems/base_pytorch_nvidia:1.0.0 + # TODO: Try to find working installation of flash attention (flash-attn<1.0.5) + setup: + - type: python + pypi: + - gdown + - scanpy + +runners: + - type: executable + - type: nextflow + directives: + label: [midtime, midmem, midcpu, gpu] diff --git a/src/methods/scfoundation/script.py b/src/methods/scfoundation/script.py new file mode 100644 index 00000000..40f43978 --- /dev/null +++ b/src/methods/scfoundation/script.py @@ -0,0 +1,70 @@ +import sys +import tempfile +import scanpy as sc +import anndata as ad +import gdown +import cellplm +import torch + +import warnings +warnings.filterwarnings("ignore") + + +import numpy as np +import anndata as ad + +from get_embeddings import * + +## VIASH START +# Note: this section is auto-generated by viash at runtime. To edit it, make changes +# in config.vsh.yaml and then run `viash config inject config.vsh.yaml`. + +model_path = "models.ckpt" + +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" + +par = { + 'input': 'resources_test/.../input.h5ad', + 'output': 'output.h5ad' +} +meta = { + 'name': 'scfoundation' +} + +sys.path.append(meta["resources_dir"]) +from read_anndata_partial import read_anndata + +print("\n>>> Reading input files...", flush=True) +print(f"Input H5AD file: '{par['input']}'", flush=True) +adata = read_anndata(par["input"], X="layers/counts", obs="obs", var="var", uns="uns") + + +print(adata, flush=True) + +print('Preprocess data', flush=True) +# ... preprocessing ... + +print('Train model', flush=True) +# ... train model ... + +embedding = main_get(data=adata, model_path=model_path) + +print('Generate predictions', flush=True) +# ... generate predictions ... + +output = ad.AnnData( + obs=adata.obs[[]], + var=adata.var[[]], + obsm={ + "X_emb": embedding, + }, + uns={ + "dataset_id": adata.uns["dataset_id"], + "normalization_id": adata.uns["normalization_id"], + "method_id": meta["name"], + }, +) +print(output) + +output.write_h5ad(par['output'], compression='gzip') + diff --git a/src/utils/OS_scRNA_gene_index.19264.tsv b/src/utils/OS_scRNA_gene_index.19264.tsv new file mode 100644 index 00000000..e02ea022 --- /dev/null +++ b/src/utils/OS_scRNA_gene_index.19264.tsv @@ -0,0 +1,19265 @@ +gene_name index +A1BG 0 +A1CF 1 +A2M 2 +A2ML1 3 +A3GALT2 4 +A4GALT 5 +A4GNT 6 +AAAS 7 +AACS 8 +AADAC 9 +AADACL2 10 +AADACL3 11 +AADACL4 12 +AADAT 13 +AAGAB 14 +AAK1 15 +AAMDC 16 +AAMP 17 +AANAT 18 +AAR2 19 +AARD 20 +AARS1 21 +AARS2 22 +AARSD1 23 +AASDH 24 +AASDHPPT 25 +AASS 26 +AATF 27 +AATK 28 +ABAT 29 +ABCA1 30 +ABCA10 31 +ABCA12 32 +ABCA13 33 +ABCA2 34 +ABCA3 35 +ABCA4 36 +ABCA5 37 +ABCA6 38 +ABCA7 39 +ABCA8 40 +ABCA9 41 +ABCB1 42 +ABCB10 43 +ABCB11 44 +ABCB4 45 +ABCB5 46 +ABCB6 47 +ABCB7 48 +ABCB8 49 +ABCB9 50 +ABCC1 51 +ABCC10 52 +ABCC11 53 +ABCC12 54 +ABCC2 55 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+ZNF827 19166 +ZNF829 19167 +ZNF83 19168 +ZNF830 19169 +ZNF831 19170 +ZNF835 19171 +ZNF836 19172 +ZNF837 19173 +ZNF839 19174 +ZNF84 19175 +ZNF841 19176 +ZNF843 19177 +ZNF844 19178 +ZNF845 19179 +ZNF846 19180 +ZNF85 19181 +ZNF850 19182 +ZNF852 19183 +ZNF853 19184 +ZNF860 19185 +ZNF862 19186 +ZNF865 19187 +ZNF875 19188 +ZNF878 19189 +ZNF879 19190 +ZNF880 19191 +ZNF888 19192 +ZNF891 19193 +ZNF90 19194 +ZNF91 19195 +ZNF92 19196 +ZNF93 19197 +ZNF98 19198 +ZNF99 19199 +ZNFX1 19200 +ZNHIT1 19201 +ZNHIT2 19202 +ZNHIT3 19203 +ZNHIT6 19204 +ZNRD2 19205 +ZNRF1 19206 +ZNRF2 19207 +ZNRF3 19208 +ZNRF4 19209 +ZP1 19210 +ZP2 19211 +ZP3 19212 +ZP4 19213 +ZPBP 19214 +ZPBP2 19215 +ZPLD1 19216 +ZPR1 19217 +ZRANB1 19218 +ZRANB2 19219 +ZRANB3 19220 +ZRSR2 19221 +ZSCAN1 19222 +ZSCAN10 19223 +ZSCAN12 19224 +ZSCAN16 19225 +ZSCAN18 19226 +ZSCAN2 19227 +ZSCAN20 19228 +ZSCAN21 19229 +ZSCAN22 19230 +ZSCAN23 19231 +ZSCAN25 19232 +ZSCAN26 19233 +ZSCAN29 19234 +ZSCAN30 19235 +ZSCAN31 19236 +ZSCAN32 19237 +ZSCAN4 19238 +ZSCAN5A 19239 +ZSCAN5B 19240 +ZSCAN5C 19241 +ZSCAN9 19242 +ZSWIM1 19243 +ZSWIM2 19244 +ZSWIM3 19245 +ZSWIM4 19246 +ZSWIM5 19247 +ZSWIM6 19248 +ZSWIM7 19249 +ZSWIM8 19250 +ZSWIM9 19251 +ZUP1 19252 +ZW10 19253 +ZWILCH 19254 +ZWINT 19255 +ZXDA 19256 +ZXDB 19257 +ZXDC 19258 +ZYG11A 19259 +ZYG11B 19260 +ZYX 19261 +ZZEF1 19262 +ZZZ3 19263 diff --git a/src/utils/get_embedding.py b/src/utils/get_embedding.py new file mode 100644 index 00000000..a56edda7 --- /dev/null +++ b/src/utils/get_embedding.py @@ -0,0 +1,259 @@ +# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited + +import argparse +import random,os +import numpy as np +import pandas as pd +import argparse +import torch +from tqdm import tqdm +import scipy.sparse +from scipy.sparse import issparse +import scanpy as sc +from load import * + + +def main_gene_selection(X_df, gene_list): + """ + Describe: + rebuild the input adata to select target genes encode protein + Parameters: + adata->`~anndata.AnnData` object: adata with var index_name by gene symbol + gene_list->list: wanted target gene + Returns: + adata_new->`~anndata.AnnData` object + to_fill_columns->list: zero padding gene + """ + to_fill_columns = list(set(gene_list) - set(X_df.columns)) + padding_df = pd.DataFrame(np.zeros((X_df.shape[0], len(to_fill_columns))), + columns=to_fill_columns, + index=X_df.index) + X_df = pd.DataFrame(np.concatenate([df.values for df in [X_df, padding_df]], axis=1), + index=X_df.index, + columns=list(X_df.columns) + list(padding_df.columns)) + X_df = X_df[gene_list] + + var = pd.DataFrame(index=X_df.columns) + var['mask'] = [1 if i in to_fill_columns else 0 for i in list(var.index)] + return X_df, to_fill_columns,var +gene_list_df = pd.read_csv('./OS_scRNA_gene_index.19264.tsv', header=0, delimiter='\t') +gene_list = list(gene_list_df['gene_name']) + +def main_get(task_name = 'bec', + input_type = 'singlecell', + output_type = 'cell', + pool_type = 'all', + tgthighres='t4', + data=None, + save_path='./', + pre_normalized='F', + demo='store_true', + version='ce', + model_path='None', + ckpt_name='01B-resolution'): + + args = argparse.Namespace() + args.task_name = task_name + args.input_type = input_type + args.output_type = output_type + args.pool_type = pool_type + args.tgthighres = tgthighres + args.data = data + args.save_path = save_path + args.pre_normalized = pre_normalized + args.demo = demo + args.version = version + args.model_path = model_path + args.ckpt_name = ckpt_name + + #Set random seed + random.seed(0) + np.random.seed(0) # numpy random generator + + torch.manual_seed(0) + torch.cuda.manual_seed_all(0) + + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + #Load data + gexpr_feature = data + idx = gexpr_feature.obs_names.tolist() + try: + col = gexpr_feature.var.gene_name.tolist() + except: + col = gexpr_feature.var_names.tolist() + if issparse(gexpr_feature.X): + gexpr_feature = gexpr_feature.X.toarray() + else: + gexpr_feature = gexpr_feature.X + gexpr_feature = pd.DataFrame(gexpr_feature,index=idx,columns=col) + + if gexpr_feature.shape[1]<19264: + print('covert gene feature into 19264') + gexpr_feature, to_fill_columns,var = main_gene_selection(gexpr_feature,gene_list) + assert gexpr_feature.shape[1]>=19264 + + if (args.pre_normalized == 'F') and (args.input_type == 'bulk'): + adata = sc.AnnData(gexpr_feature) + sc.pp.normalize_total(adata) + sc.pp.log1p(adata) + gexpr_feature = pd.DataFrame(adata.X,index=adata.obs_names,columns=adata.var_names) + + if args.demo: + gexpr_feature = gexpr_feature.iloc[:10,:] + print(gexpr_feature.shape) + + #Load model + if args.version == 'noversion': + ckpt_path = args.model_path + key=None + else: + ckpt_path = './models/models.ckpt' + if args.output_type == 'cell': + if args.version == 'ce': + key = 'cell' + elif args.version == 'rde': + key = 'rde' + else: + raise ValueError('No version found') + elif args.output_type == 'gene': + key = 'gene' + elif args.output_type == 'gene_batch': + key = 'gene' + elif args.output_type == 'gene_expression': # Not recommended + key = 'gene' + else: + raise ValueError('output_mode must be one of cell gene, gene_batch, gene_expression') + pretrainmodel,pretrainconfig = load_model_frommmf(ckpt_path,key) + pretrainmodel.eval() + + geneexpemb=[] + batchcontainer = [] + strname = os.path.join(args.save_path, args.task_name +'_'+ args.ckpt_name +"_"+ args.input_type + '_' + args.output_type + '_embedding_' + args.tgthighres + '_resolution.npy') + print('save at {}'.format(strname)) + + #Inference + for i in tqdm(range(gexpr_feature.shape[0])): + with torch.no_grad(): + #Bulk + if args.input_type == 'bulk': + if args.pre_normalized == 'T': + totalcount = gexpr_feature.iloc[i,:].sum() + elif args.pre_normalized == 'F': + totalcount = np.log10(gexpr_feature.iloc[i,:].sum()) + else: + raise ValueError('pre_normalized must be T or F') + tmpdata = (gexpr_feature.iloc[i,:]).tolist() + pretrain_gene_x = torch.tensor(tmpdata+[totalcount,totalcount]).unsqueeze(0).cuda() + data_gene_ids = torch.arange(19266, device=pretrain_gene_x.device).repeat(pretrain_gene_x.shape[0], 1) + + #Single cell + elif args.input_type == 'singlecell': + # pre-Normalization + if args.pre_normalized == 'F': + tmpdata = (np.log1p(gexpr_feature.iloc[i,:]/(gexpr_feature.iloc[i,:].sum())*1e4)).tolist() + elif args.pre_normalized == 'T': + tmpdata = (gexpr_feature.iloc[i,:]).tolist() + elif args.pre_normalized == 'A': + tmpdata = (gexpr_feature.iloc[i,:-1]).tolist() + else: + raise ValueError('pre_normalized must be T,F or A') + + if args.pre_normalized == 'A': + totalcount = gexpr_feature.iloc[i,-1] + else: + totalcount = gexpr_feature.iloc[i,:].sum() + + # select resolution + if args.tgthighres[0] == 'f': + pretrain_gene_x = torch.tensor(tmpdata+[np.log10(totalcount*float(args.tgthighres[1:])),np.log10(totalcount)]).unsqueeze(0).cuda() + elif args.tgthighres[0] == 'a': + pretrain_gene_x = torch.tensor(tmpdata+[np.log10(totalcount)+float(args.tgthighres[1:]),np.log10(totalcount)]).unsqueeze(0).cuda() + elif args.tgthighres[0] == 't': + pretrain_gene_x = torch.tensor(tmpdata+[float(args.tgthighres[1:]),np.log10(totalcount)]).unsqueeze(0).cuda() + else: + raise ValueError('tgthighres must be start with f, a or t') + data_gene_ids = torch.arange(19266, device=pretrain_gene_x.device).repeat(pretrain_gene_x.shape[0], 1) + + value_labels = pretrain_gene_x > 0 + x, x_padding = gatherData(pretrain_gene_x, value_labels, pretrainconfig['pad_token_id']) + + #Cell embedding + if args.output_type=='cell': + position_gene_ids, _ = gatherData(data_gene_ids, value_labels, pretrainconfig['pad_token_id']) + x = pretrainmodel.token_emb(torch.unsqueeze(x, 2).float(), output_weight = 0) + position_emb = pretrainmodel.pos_emb(position_gene_ids) + x += position_emb + geneemb = pretrainmodel.encoder(x,x_padding) + + geneemb1 = geneemb[:,-1,:] + geneemb2 = geneemb[:,-2,:] + geneemb3, _ = torch.max(geneemb[:,:-2,:], dim=1) + geneemb4 = torch.mean(geneemb[:,:-2,:], dim=1) + if args.pool_type=='all': + geneembmerge = torch.concat([geneemb1,geneemb2,geneemb3,geneemb4],axis=1) + elif args.pool_type=='max': + geneembmerge, _ = torch.max(geneemb, dim=1) + else: + raise ValueError('pool_type must be all or max') + geneexpemb.append(geneembmerge.detach().cpu().numpy()) + + #Gene embedding + elif args.output_type=='gene': + pretrainmodel.to_final = None + encoder_data, encoder_position_gene_ids, encoder_data_padding, encoder_labels, decoder_data, decoder_data_padding, new_data_raw, data_mask_labels, decoder_position_gene_ids = getEncoerDecoderData(pretrain_gene_x.float(),pretrain_gene_x.float(),pretrainconfig) + out = pretrainmodel.forward(x=encoder_data, padding_label=encoder_data_padding, + encoder_position_gene_ids=encoder_position_gene_ids, + encoder_labels=encoder_labels, + decoder_data=decoder_data, + mask_gene_name=False, + mask_labels=None, + decoder_position_gene_ids=decoder_position_gene_ids, + decoder_data_padding_labels=decoder_data_padding, + ) + out = out[:,:19264,:].contiguous() + geneexpemb.append(out.detach().cpu().numpy()) + + #Gene batch embedding + elif args.output_type=='gene_batch': + batchcontainer.append(pretrain_gene_x.float()) + if len(batchcontainer)==gexpr_feature.shape[0]: + batchcontainer = torch.concat(batchcontainer,axis=0) + else: + continue + pretrainmodel.to_final = None + encoder_data, encoder_position_gene_ids, encoder_data_padding, encoder_labels, decoder_data, decoder_data_padding, new_data_raw, data_mask_labels, decoder_position_gene_ids = getEncoerDecoderData(batchcontainer,batchcontainer,pretrainconfig) + out = pretrainmodel.forward(x=encoder_data, padding_label=encoder_data_padding, + encoder_position_gene_ids=encoder_position_gene_ids, + encoder_labels=encoder_labels, + decoder_data=decoder_data, + mask_gene_name=False, + mask_labels=None, + decoder_position_gene_ids=decoder_position_gene_ids, + decoder_data_padding_labels=decoder_data_padding, + ) + geneexpemb = out[:,:19264,:].contiguous().detach().cpu().numpy() + #Gene_expression + elif args.output_type=='gene_expression': + encoder_data, encoder_position_gene_ids, encoder_data_padding, encoder_labels, decoder_data, decoder_data_padding, new_data_raw, data_mask_labels, decoder_position_gene_ids = getEncoerDecoderData(pretrain_gene_x.float(),pretrain_gene_x.float(),pretrainconfig) + out = pretrainmodel.forward(x=encoder_data, padding_label=encoder_data_padding, + encoder_position_gene_ids=encoder_position_gene_ids, + encoder_labels=encoder_labels, + decoder_data=decoder_data, + mask_gene_name=False, + mask_labels=None, + decoder_position_gene_ids=decoder_position_gene_ids, + decoder_data_padding_labels=decoder_data_padding, + ) + out = out[:,:19264].contiguous() + geneexpemb.append(out.detach().cpu().numpy()) + else: + raise ValueError('output_type must be cell or gene or gene_batch or gene_expression') + geneexpemb = np.squeeze(np.array(geneexpemb)) + print(geneexpemb.shape) + np.save(strname,geneexpemb) + + return geneexpemb + diff --git a/src/utils/load.py b/src/utils/load.py new file mode 100644 index 00000000..fe26ae0b --- /dev/null +++ b/src/utils/load.py @@ -0,0 +1,198 @@ +# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited + +import torch +import sys +import os +import numpy as np +import random +from pretrainmodels import select_model +import math + +def next_16x(x): + return int(math.ceil(x / 16) * 16) + +def seed_all(seed, cuda_deterministic=False): + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + if cuda_deterministic: # slower, more reproducible + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + else: + torch.backends.cudnn.deterministic = False + torch.backends.cudnn.benchmark = True + +def gatherDatanopad(data, labels, pad_token_id): + max_num = labels.sum(1) + none_labels = ~labels + labels = labels.float() + labels[none_labels] = torch.tensor(-float('Inf'), device=labels.device) + + tmp_data = torch.tensor([(i + 1) * 20000 for i in range(labels.shape[1], 0, -1)], device=labels.device) + labels += tmp_data + + + fake_label_gene_idx = labels.topk(max_num).indices + + new_data = torch.gather(data, 1, fake_label_gene_idx) + + padding_labels = (new_data == pad_token_id) + + return new_data, padding_labels + +def gatherData(data, labels, pad_token_id): + value_nums = labels.sum(1) + max_num = max(value_nums) + + + fake_data = torch.full((data.shape[0], max_num), pad_token_id, + device=data.device) + data = torch.hstack([data, fake_data]) + + fake_label = torch.full((labels.shape[0], max_num), 1, + device=labels.device) + none_labels = ~labels + labels = labels.float() + labels[none_labels] = torch.tensor(-float('Inf'), device=labels.device) + + tmp_data = torch.tensor([(i + 1) * 20000 for i in range(labels.shape[1], 0, -1)], device=labels.device) + labels += tmp_data + + labels = torch.hstack([labels, fake_label]) + + fake_label_gene_idx = labels.topk(max_num).indices + + new_data = torch.gather(data, 1, fake_label_gene_idx) + + padding_labels = (new_data == pad_token_id) + + return new_data, padding_labels + + +def convertconfig(ckpt): + newconfig = {} + newconfig['config']={} + model_type = ckpt['config']['model'] + + for key, val in ckpt['config']['model_config'][model_type].items(): + newconfig['config'][key]=val + + for key, val in ckpt['config']['dataset_config']['rnaseq'].items(): + newconfig['config'][key]=val + + if model_type == 'performergau_resolution': + model_type = 'performer_gau' + + import collections + d = collections.OrderedDict() + for key, val in ckpt['state_dict'].items(): + d[str(key).split('model.')[1]]=val + + newconfig['config']['model_type']=model_type + newconfig['model_state_dict']=d + newconfig['config']['pos_embed']=False + newconfig['config']['device']='cuda' + return newconfig + +def load_model(best_ckpt_path, device): + model_data = torch.load(best_ckpt_path,map_location=device) + if not model_data.__contains__('config'): + print('***** No config *****') + config={} + config['model_type']='flash_all' + else: + config=model_data['config'] + print(config) + if not config.__contains__('qv_dim'): + if config['model'] != 'mae_autobin': + if config.__contains__('dim_head'): + config['qv_dim']=config['dim_head'] + else: + print('***** No qv_dim ***** set 64') + config['qv_dim']= 64 + if not config.__contains__('ppi_edge'): + config['ppi_edge']=None + model = select_model(config) + model_state_dict = model_data['model_state_dict'] + model.load_state_dict(model_state_dict) + return model.cuda(),config + +def load_model_frommmf(best_ckpt_path, key='gene'): + model_data = torch.load(best_ckpt_path,map_location='cpu') + model_data = model_data[key] + model_data = convertconfig(model_data) + if not model_data.__contains__('config'): + print('***** No config *****') + config={} + config['model_type']='flash_all' + else: + config=model_data['config'] + print(config) + if not config.__contains__('qv_dim'): + if config['model'] != 'mae_autobin': + if config.__contains__('dim_head'): + config['qv_dim']=config['dim_head'] + else: + print('***** No qv_dim ***** set 64') + config['qv_dim']= 64 + if not config.__contains__('ppi_edge'): + config['ppi_edge']=None + model = select_model(config) + model_state_dict = model_data['model_state_dict'] + model.load_state_dict(model_state_dict) + return model.cuda(),config + +def main_gene_selection(X_df, gene_list): + """ + Describe: + rebuild the input adata to select target genes encode protein + Parameters: + adata->`~anndata.AnnData` object: adata with var index_name by gene symbol + gene_list->list: wanted target gene + Returns: + adata_new->`~anndata.AnnData` object + to_fill_columns->list: zero padding gene + """ + to_fill_columns = list(set(gene_list) - set(X_df.columns)) + padding_df = pd.DataFrame(np.zeros((X_df.shape[0], len(to_fill_columns))), + columns=to_fill_columns, + index=X_df.index) + X_df = pd.DataFrame(np.concatenate([df.values for df in [X_df, padding_df]], axis=1), + index=X_df.index, + columns=list(X_df.columns) + list(padding_df.columns)) + X_df = X_df[gene_list] + + var = pd.DataFrame(index=X_df.columns) + var['mask'] = [1 if i in to_fill_columns else 0 for i in list(var.index)] + return X_df, to_fill_columns + +def get_genename(): + gene_list_df = pd.read_csv('./OS_scRNA_gene_index.19264.tsv', header=0, delimiter='\t') + return list(gene_list_df['gene_name']) + +def save_AUCs(AUCs, filename): + with open(filename, 'a') as f: + f.write('\t'.join(map(str, AUCs)) + '\n') + +def getEncoerDecoderData(data, data_raw, config): + decoder_data = data.clone().detach() + decoder_data_padding = torch.full_like(data, False, dtype=torch.bool).to(data.device) + + encoder_data_labels = data_raw > 0 + encoder_data, encoder_data_padding = gatherData(decoder_data, encoder_data_labels, + config['pad_token_id']) + new_data_raw = data_raw + data_gene_ids = torch.arange(data.shape[1], device=data.device).repeat(data.shape[0], 1) + encoder_position_gene_ids, _ = gatherData(data_gene_ids, encoder_data_labels, + config['pad_token_id']) + decoder_position_gene_ids = data_gene_ids + data_mask_labels = None + + encoder_position_gene_ids[encoder_data_padding] = config["seq_len"] + decoder_position_gene_ids[decoder_data_padding] = config["seq_len"] + + return encoder_data, encoder_position_gene_ids, encoder_data_padding, encoder_data_labels, decoder_data, decoder_data_padding, new_data_raw, data_mask_labels, decoder_position_gene_ids diff --git a/src/utils/pretrainmodels/__init__.py b/src/utils/pretrainmodels/__init__.py new file mode 100644 index 00000000..f147901f --- /dev/null +++ b/src/utils/pretrainmodels/__init__.py @@ -0,0 +1,3 @@ +# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited + +from pretrainmodels.select_model import select_model diff --git a/src/utils/pretrainmodels/mae_autobin.py b/src/utils/pretrainmodels/mae_autobin.py new file mode 100644 index 00000000..953b4c91 --- /dev/null +++ b/src/utils/pretrainmodels/mae_autobin.py @@ -0,0 +1,162 @@ +# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited + +import math +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from torch.cuda.amp import autocast +from einops import rearrange, repeat + +from functools import partial +from contextlib import contextmanager + + +def exists(val): + return val is not None + +class AutoDiscretizationEmbedding2(nn.Module): + def __init__(self, dim, max_seq_len, bin_num, bin_alpha, mask_token_id = None, pad_token_id = None): + super().__init__() + + self.dim = dim + self.max_seq_len = max_seq_len + self.bin_num = bin_num + self.bin_alpha = bin_alpha + + self.mlp = nn.Linear(1, self.bin_num) + self.mlp2 = nn.Linear(self.bin_num, self.bin_num) + self.LeakyReLU = nn.LeakyReLU(0.1) + self.Softmax = nn.Softmax(dim=-1) + self.emb = nn.Embedding(self.bin_num, self.dim) + + self.emb_mask = nn.Embedding(1, self.dim) + self.emb_pad = nn.Embedding(1, self.dim) + + self.bin_num_idx = torch.tensor(range(self.bin_num)) + self.mask_token_id = mask_token_id + self.pad_token_id = pad_token_id + # print('self.bin_num_idx',self.bin_num_idx, self.bin_num_idx.shape) + + self.tensor0 = torch.tensor(0, dtype=torch.long) + + def forward(self, x, output_weight=0): + x_mask_idx = (x==self.mask_token_id).nonzero() + x_pad_idx = (x==self.pad_token_id).nonzero() + # print("x_mask",x_mask_idx.shape,x_mask_idx) + + x = self.mlp(x) # [B,N,1] -> [B,N,H] + x = self.LeakyReLU(x) # [B,N,H] + x_crosslayer = self.mlp2(x) # [B,N,H] + x = self.bin_alpha * x + x_crosslayer # [B,N,H] + weight = self.Softmax(x) # [B, N, H] + # print('weight', weight.shape, weight, torch.sum(weight, 2)) + + bin_num_idx = self.bin_num_idx.to(x.device) # [H,] + # print('bin_num_idx', bin_num_idx.shape) + + token_emb = self.emb(bin_num_idx) # [H, D] + # print('token_emb', token_emb.shape) + x = torch.matmul(weight, token_emb) #[B, N, D] + + # print("x_emb",x.shape,x) + + tensor0 = torch.tensor(0, dtype=torch.long, device=x.device) + + mask_token_emb = self.emb_mask(tensor0).to(x.device).type(x.dtype) + # print(mask_token_emb.dtype) + # print("x", x.dtype) + x[x_mask_idx[:,0],x_mask_idx[:,1],:] = mask_token_emb.repeat(x_mask_idx.shape[0],1) + # print("x_emb",x.shape,x) + + pad_token_emb = self.emb_pad(tensor0).to(x.device).type(x.dtype) + x[x_pad_idx[:,0],x_pad_idx[:,1],:] = pad_token_emb.repeat(x_pad_idx.shape[0],1) + + if output_weight: + return x,weight + return x + +class RandomPositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + + def forward(self, x): + t = torch.arange(x.shape[1], device=x.device) + return self.emb(t) + + +class MaeAutobin(nn.Module): + def __init__( + self, + *, + num_tokens, # num of tokens + max_seq_len, # max length of sequence + embed_dim, # encoder dim of tokens + decoder_embed_dim, + tie_embed=False, + bin_alpha = 1.0, + bin_num = 10, + pad_token_id = None, + mask_token_id = None, + ): + super(MaeAutobin, self).__init__() + + self.max_seq_len = max_seq_len + self.num_tokens = num_tokens + self.pad_token_id = pad_token_id + self.mask_token_id = mask_token_id + + # encoder + self.token_emb = AutoDiscretizationEmbedding2(embed_dim, max_seq_len, bin_num=bin_num, bin_alpha=bin_alpha, pad_token_id=self.pad_token_id, mask_token_id=self.mask_token_id) + self.pos_emb = nn.Embedding(max_seq_len+1, embed_dim) #RandomPositionalEmbedding(embed_dim, max_seq_len) + + # ## DEBUG + self.encoder = None + + ##### decoder + self.decoder = None + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + self.norm = nn.LayerNorm(decoder_embed_dim) + self.to_final = nn.Linear(decoder_embed_dim, 1) + + def forward(self, x, padding_label, encoder_position_gene_ids, encoder_labels, decoder_data, + mask_gene_name, mask_labels, decoder_position_gene_ids, decoder_data_padding_labels, + output_attentions=False, **kwargs): + b, n, device = *x.shape, x.device + assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' + + # token and positional embedding + x = self.token_emb(torch.unsqueeze(x, 2), output_weight = 0) + if output_attentions: + x.requires_grad_() # used for attn_map output + + position_emb = self.pos_emb(encoder_position_gene_ids) + x += position_emb + x = self.encoder(x, padding_mask=padding_label) + + decoder_data = self.token_emb(torch.unsqueeze(decoder_data, 2)) + position_emb = self.pos_emb(decoder_position_gene_ids) + if mask_gene_name: + # todo + # mask gene_name + print('mask_gene_name not done') + exit(0) + batch_idx, gen_idx = (encoder_labels == True).nonzero(as_tuple=True) + decoder_data[batch_idx, gen_idx] = x[~padding_label].to(decoder_data.dtype) + + decoder_data += position_emb + + decoder_data = self.decoder_embed(decoder_data) + x = self.decoder(decoder_data, padding_mask=decoder_data_padding_labels) + + # print("x0",x.shape) + x = self.norm(x) + # print("x1",x.shape) + if exists(self.to_final): + x = self.to_final(x) + return x.squeeze(2) + else: + return x + + return x diff --git a/src/utils/pretrainmodels/performer.py b/src/utils/pretrainmodels/performer.py new file mode 100644 index 00000000..500679d1 --- /dev/null +++ b/src/utils/pretrainmodels/performer.py @@ -0,0 +1,638 @@ +## Modeified code from https://github.com/TencentAILabHealthcare/scBERT +import math +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from torch.cuda.amp import autocast +from einops import rearrange, repeat + +from functools import partial +from contextlib import contextmanager + +from local_attention import LocalAttention +from pretrainmodels.reversible import ReversibleSequence, SequentialSequence + +try: + from apex import amp + APEX_AVAILABLE = True +except: + APEX_AVAILABLE = False + +# helpers + +def exists(val): + return val is not None + +def empty(tensor): + return tensor.numel() == 0 + +def default(val, d): + return val if exists(val) else d + +@contextmanager +def null_context(): + yield + +def cast_tuple(val): + return (val,) if not isinstance(val, tuple) else val + +# def get_module_device(module): +# return next(module.parameters).device + +def get_module_device(module): + try: + return next(module.parameters()).device + except StopIteration: + # For nn.DataParallel compatibility in PyTorch 1.5 + def find_tensor_attributes(module): + tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)] + return tuples + gen = module._named_members(get_members_fn=find_tensor_attributes) + first_tuple = next(gen) + return first_tuple[1].device + +def find_modules(nn_module, type): + return [module for module in nn_module.modules() if isinstance(module, type)] + +class Always(nn.Module): + def __init__(self, val): + super().__init__() + self.val = val + + def forward(self, *args, **kwargs): + return self.val + +# kernel functions + +# transcribed from jax to pytorch from +# https://github.com/google-research/google-research/blob/master/performer/fast_attention/jax/fast_attention.py + +def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device = None): + b, h, *_ = data.shape + + data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1. + + ratio = (projection_matrix.shape[0] ** -0.5) + + projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h) + projection = projection.type_as(data) + + data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection) + + diag_data = data ** 2 + diag_data = torch.sum(diag_data, dim=-1) + diag_data = (diag_data / 2.0) * (data_normalizer ** 2) + diag_data = diag_data.unsqueeze(dim=-1) + + if is_query: + data_dash = ratio * ( + torch.exp(data_dash - diag_data - + torch.max(data_dash, dim=-1, keepdim=True).values) + eps) + else: + data_dash = ratio * ( + torch.exp(data_dash - diag_data - torch.max(data_dash)) + eps) + + return data_dash.type_as(data) + +def generalized_kernel(data, *, projection_matrix, kernel_fn = nn.ReLU(), kernel_epsilon = 0.001, normalize_data = True, device = None): + b, h, *_ = data.shape + + data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1. + + if projection_matrix is None: + return kernel_fn(data_normalizer * data) + kernel_epsilon + + projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h) + projection = projection.type_as(data) + + data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection) + + data_prime = kernel_fn(data_dash) + kernel_epsilon + return data_prime.type_as(data) + +def orthogonal_matrix_chunk(cols, device = None): + unstructured_block = torch.randn((cols, cols), device = device) + q, r = torch.linalg.qr(unstructured_block.cpu(), mode = 'reduced') + q, r = map(lambda t: t.to(device), (q, r)) + return q.t() + +def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling = 0, device = None): + nb_full_blocks = int(nb_rows / nb_columns) + + block_list = [] + + for _ in range(nb_full_blocks): + q = orthogonal_matrix_chunk(nb_columns, device = device) + block_list.append(q) + + remaining_rows = nb_rows - nb_full_blocks * nb_columns + if remaining_rows > 0: + q = orthogonal_matrix_chunk(nb_columns, device = device) + block_list.append(q[:remaining_rows]) + + final_matrix = torch.cat(block_list) + + if scaling == 0: + multiplier = torch.randn((nb_rows, nb_columns), device = device).norm(dim = 1) + elif scaling == 1: + multiplier = math.sqrt((float(nb_columns))) * torch.ones((nb_rows,), device = device) + else: + raise ValueError(f'Invalid scaling {scaling}') + + return torch.diag(multiplier) @ final_matrix + +# linear attention classes with softmax kernel + +# non-causal linear attention +def linear_attention(q, k, v): + k_cumsum = k.sum(dim = -2) + D_inv = 1. / torch.einsum('...nd,...d->...n', q, k_cumsum.type_as(q)) + context = torch.einsum('...nd,...ne->...de', k, v) + out = torch.einsum('...de,...nd,...n->...ne', context, q, D_inv) + return out + +# efficient causal linear attention, created by EPFL +def causal_linear_attention(q, k, v, eps = 1e-6): + from fast_transformers.causal_product import CausalDotProduct + autocast_enabled = torch.is_autocast_enabled() + is_half = isinstance(q, torch.cuda.HalfTensor) + assert not is_half or APEX_AVAILABLE, 'half tensors can only be used if nvidia apex is available' + cuda_context = null_context if not autocast_enabled else partial(autocast, enabled = False) + + causal_dot_product_fn = amp.float_function(CausalDotProduct.apply) if is_half else CausalDotProduct.apply + + k_cumsum = k.cumsum(dim=-2) + eps + D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q)) + + with cuda_context(): + if autocast_enabled: + q, k, v = map(lambda t: t.float(), (q, k, v)) + + out = causal_dot_product_fn(q, k, v) + + out = torch.einsum('...nd,...n->...nd', out, D_inv) + return out + +# inefficient causal linear attention, without cuda code, for reader's reference +# not being used +def causal_linear_attention_noncuda(q, k, v, chunk_size = 128): + last_k_cumsum = 0 + last_context_cumsum = 0 + outs = [] + + for q, k, v in zip(*map(lambda t: t.chunk(chunk_size, dim = -2), (q, k, v))): + k_cumsum = last_k_cumsum + k.cumsum(dim=-2) + + D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q)) + context = torch.einsum('...nd,...ne->...nde', k, v) + context_cumsum = last_context_cumsum + context.cumsum(dim=-3) + out = torch.einsum('...nde,...nd,...n->...ne', context_cumsum, q, D_inv) + + last_k_cumsum = k_cumsum[:, :, -1:] + last_context_cumsum = context_cumsum[:, :, -1:] + outs.append(out) + + return torch.cat(outs, dim = -2) + +def norm_tensor(tensor, dim=-1): + return tensor / tensor.sum(dim=dim).unsqueeze(dim) + +class FastAttention(nn.Module): + def __init__(self, dim_heads, nb_features = None, ortho_scaling = 0, causal = False, generalized_attention = False, kernel_fn = nn.ReLU(), no_projection = False): + super().__init__() + nb_features = default(nb_features, int(dim_heads * math.log(dim_heads))) + + self.dim_heads = dim_heads + self.nb_features = nb_features + self.ortho_scaling = ortho_scaling + + self.create_projection = partial(gaussian_orthogonal_random_matrix, nb_rows = self.nb_features, nb_columns = dim_heads, scaling = ortho_scaling) + projection_matrix = self.create_projection() + self.register_buffer('projection_matrix', projection_matrix) + + self.generalized_attention = generalized_attention + self.kernel_fn = kernel_fn + + self.no_projection = no_projection + + self.causal = causal + if causal: + try: + import fast_transformers.causal_product.causal_product_cuda + self.causal_linear_fn = partial(causal_linear_attention) + except ImportError: + print('unable to import cuda code for auto-regressive Performer. will default to the memory inefficient non-cuda version') + self.causal_linear_fn = causal_linear_attention_noncuda + + @torch.no_grad() + def redraw_projection_matrix(self, device): + projections = self.create_projection(device = device) + self.projection_matrix.copy_(projections) + del projections + + def forward(self, q, k, v, output_attentions = False): + device = q.device + if self.no_projection: + q = q.softmax(dim = -1) + k = torch.exp(k) if self.causal else k.softmax(dim = -2) + + elif self.generalized_attention: + create_kernel = partial(generalized_kernel, kernel_fn = self.kernel_fn, projection_matrix = self.projection_matrix, device = device) + q, k = map(create_kernel, (q, k)) + + else: + create_kernel = partial(softmax_kernel, projection_matrix = self.projection_matrix, device = device) + q = create_kernel(q, is_query = True) + k = create_kernel(k, is_query = False) + + attn_fn = linear_attention if not self.causal else self.causal_linear_fn + out = attn_fn(q, k, v) + if output_attentions: + v_diag = torch.eye(v.shape[-2]).to(device) + v_diag = v_diag.unsqueeze(0).unsqueeze(0).repeat(v.shape[0],v.shape[1],1,1) + attn_weights = torch.zeros(1, q.shape[1], q.shape[2], q.shape[2]).to('cpu').to(torch.float16) + for head_dim in range(q.shape[1]): + attn_weights[0, head_dim] = attn_fn(q[:,head_dim].to(torch.float16), k[:,head_dim].to(torch.float16), v_diag[:,head_dim].to(torch.float16)).detach().cpu() + attn_weights /= q.shape[1] + return out, attn_weights + else: + return out + +# classes + +class ReZero(nn.Module): + def __init__(self, fn): + super().__init__() + self.g = nn.Parameter(torch.tensor(1e-3)) + self.fn = fn + + def forward(self, x, **kwargs): + return self.fn(x, **kwargs) * self.g + +class PreScaleNorm(nn.Module): + def __init__(self, dim, fn, eps=1e-5): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.ones(1)) + self.eps = eps + + def forward(self, x, **kwargs): + n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps) + x = x / n * self.g + return self.fn(x, **kwargs) + +class PreLayerNorm(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + def forward(self, x, **kwargs): + + return self.fn(self.norm(x), **kwargs) + +class Chunk(nn.Module): + def __init__(self, chunks, fn, along_dim = -1): + super().__init__() + self.dim = along_dim + self.chunks = chunks + self.fn = fn + + def forward(self, x, **kwargs): + if self.chunks == 1: + return self.fn(x, **kwargs) + chunks = x.chunk(self.chunks, dim = self.dim) + return torch.cat([self.fn(c, **kwargs) for c in chunks], dim = self.dim) + +class FeedForward(nn.Module): + def __init__(self, dim, mult = 4, dropout = 0., activation = None, glu = False): + super().__init__() + activation = default(activation, nn.GELU) + + self.glu = glu + self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1)) + self.act = activation() + self.dropout = nn.Dropout(dropout) + self.w2 = nn.Linear(dim * mult, dim) + + def forward(self, x, **kwargs): + if not self.glu: + x = self.w1(x) + x = self.act(x) + else: + x, v = self.w1(x).chunk(2, dim=-1) + x = self.act(x) * v + + x = self.dropout(x) + x = self.w2(x) + return x + +class SelfAttention(nn.Module): + def __init__( + self, + dim, + causal = False, + heads = 8, + dim_head = 64, + local_heads = 0, + local_window_size = 256, + nb_features = None, + feature_redraw_interval = 1000, + generalized_attention = False, + kernel_fn = nn.ReLU(), + dropout = 0., + no_projection = False, + qkv_bias = False + ): + super().__init__() + assert dim % heads == 0, 'dimension must be divisible by number of heads' + dim_head = default(dim_head, dim // heads) + inner_dim = dim_head * heads + self.fast_attention = FastAttention(dim_head, nb_features, causal = causal, generalized_attention = generalized_attention, kernel_fn = kernel_fn, no_projection = no_projection) + + self.heads = heads + self.global_heads = heads - local_heads + self.local_attn = LocalAttention(window_size = local_window_size, causal = causal, autopad = True, dropout = dropout, look_forward = int(not causal), rel_pos_emb_config = (dim_head, local_heads)) if local_heads > 0 else None + + self.to_q = nn.Linear(dim, inner_dim, bias = qkv_bias) + self.to_k = nn.Linear(dim, inner_dim, bias = qkv_bias) + self.to_v = nn.Linear(dim, inner_dim, bias = qkv_bias) + self.to_out = nn.Linear(inner_dim, dim) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, pos_emb = None, context = None, mask = None, context_mask = None, output_attentions = False, **kwargs): + b, n, _, h, gh = *x.shape, self.heads, self.global_heads + + cross_attend = exists(context) + + context = default(context, x) + context_mask = default(context_mask, mask) if not cross_attend else context_mask + + q, k, v = self.to_q(x), self.to_k(context), self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v)) + (q, lq), (k, lk), (v, lv) = map(lambda t: (t[:, :gh], t[:, gh:]), (q, k, v)) + + attn_outs = [] + + if not empty(q): + if exists(context_mask): + global_mask = context_mask[:, None, :, None] + v.masked_fill_(~global_mask, 0.) + + if exists(pos_emb) and not cross_attend: + q, k, = apply_rotary_pos_emb(q, k, pos_emb) + + if output_attentions: + out, attn_weights = self.fast_attention(q, k, v, output_attentions) + else: + out = self.fast_attention(q, k, v) + attn_outs.append(out) + + if not empty(lq): + assert not cross_attend, 'local attention is not compatible with cross attention' + out = self.local_attn(lq, lk, lv, input_mask = mask) + attn_outs.append(out) + + out = torch.cat(attn_outs, dim = 1) # combine attn_out and cross_attn_out, here we have only attn_out, that means this line does nothing + out = rearrange(out, 'b h n d -> b n (h d)') + out = self.to_out(out) + if output_attentions: + return self.dropout(out), attn_weights + else: + return self.dropout(out) + +# positional embeddings + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + + def forward(self, x): + t = torch.arange(x.shape[1], device=x.device) + return self.emb(t) + +# rotary positional embedding helpers + +def rotate_every_two(x): + x = rearrange(x, '... (d j) -> ... d j', j = 2) + x1, x2 = x.unbind(dim = -1) + x = torch.stack((-x2, x1), dim = -1) + return rearrange(x, '... d j -> ... (d j)') + +def apply_rotary_pos_emb(q, k, sinu_pos): + sinu_pos = rearrange(sinu_pos, '() n (j d) -> n j d', j = 2) + sin, cos = sinu_pos.unbind(dim = -2) + sin, cos = map(lambda t: repeat(t, 'b n -> b (n j)', j = 2), (sin, cos)) + q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k)) + return q, k + +# sinusoidal positional embeddings + +class Gene2VecPositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + gene2vec_weight = np.load('../data/gene2vec_16906.npy') + gene2vec_weight = np.concatenate((gene2vec_weight, np.zeros((1, gene2vec_weight.shape[1]))), axis=0) + gene2vec_weight = torch.from_numpy(gene2vec_weight) + self.emb = nn.Embedding.from_pretrained(gene2vec_weight) + + def forward(self, x): + t = torch.arange(x.shape[1], device=x.device) + print("t", t) + return self.emb(t) + +class Gene2VecPositionalEmbeddingIdx(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + gene2vec_weight = np.load('/nfs_beijing/minsheng/scbig/data/biomap_gene2vec.npy') + gene2vec_weight = np.concatenate((np.zeros((1, gene2vec_weight.shape[1])),gene2vec_weight),axis=0) + gene2vec_weight = gene2vec_weight[:,:dim] + gene2vec_weight = torch.Tensor(gene2vec_weight) + self.emb = nn.Embedding.from_pretrained(gene2vec_weight,freeze=False) + + def forward(self, x): + return self.emb(x) + + +class RandomPositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + gene2vec_weight = np.random.rand(max_seq_len-1, dim) + gene2vec_weight = np.concatenate((gene2vec_weight, np.zeros((1, gene2vec_weight.shape[1]))), axis=0) + gene2vec_weight = torch.from_numpy(gene2vec_weight) + self.emb = nn.Embedding.from_pretrained(gene2vec_weight, freeze=False) + + def forward(self, x): + t = torch.arange(x.shape[1], device=x.device) + return self.emb(t) + + +class RandomPositionalEmbeddingIdx(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + gene2vec_weight = np.random.rand(max_seq_len, dim) + gene2vec_weight = np.concatenate((np.zeros((1, gene2vec_weight.shape[1])),gene2vec_weight),axis=0) + gene2vec_weight = torch.from_numpy(gene2vec_weight) + self.emb = nn.Embedding.from_pretrained(gene2vec_weight, freeze=False) + + def forward(self, x): + return self.emb(x) + +# performer + +class Performer(nn.Module): + def __init__( + self, + dim, # dimension + depth, # layers + heads, # heads + dim_head, # dim of head + local_attn_heads = 0, # num of local attention heads, (heads - local_attn_heads) is num of global performers + local_window_size = 256, # window size of local attention + causal = False, # autoregressive or not + ff_mult = 4, # dim of intermediate features after attention / dim of input features + nb_features = None, # number of random features, if not set, will default to (d * log(d)), where d is the dimension of each head ?? what is random feature ?? + feature_redraw_interval = 1000, # how frequently to redraw the projection matrix, the more frequent, the slower the training + reversible = False, # reversible layers, from Reformer (save memory) + ff_chunks = 1, # chunk feedforward layer, from Reformer + generalized_attention = False, # defaults to softmax approximation, but can be set to True for generalized attention ?? what is generalized attention ?? + kernel_fn = nn.ReLU(), # the kernel function to be used, if generalized attention is turned on, defaults to Relu + use_scalenorm = False, # use scale norm, from 'Transformers without Tears' paper, a substitute for LayerNorm, priority: scalenorm.rezero.layernorm + use_rezero = False, # use Rezero or not, from 'Rezero is all you need' paper, a substitute for LayerNorm, priority: scalenorm.rezero.layernorm + ff_glu = False, # use GLU (Gated Linear Units) variant for feedforward + ff_dropout = 0., # feedforward dropout + attn_dropout = 0., # post-attention dropout + cross_attend = False, # ?? + no_projection = False, # ?? + auto_check_redraw = True, # ?? + qkv_bias = True, # ?? + ): + super().__init__() + layers = nn.ModuleList([]) + local_attn_heads = cast_tuple(local_attn_heads) + local_attn_heads = local_attn_heads * depth if len(local_attn_heads) == 1 else local_attn_heads + assert len(local_attn_heads) == depth, 'tuple specifying number of local attention heads per depth must be equal to the total depth' + assert all(map(lambda n: n >= 0 and n <= heads, local_attn_heads)), 'local attention head value must be less than the total number of heads' + + if use_scalenorm: + wrapper_fn = partial(PreScaleNorm, dim) + elif use_rezero: + wrapper_fn = ReZero + else: + wrapper_fn = partial(PreLayerNorm, dim) + + for _, local_heads in zip(range(depth), local_attn_heads): + layers.append(nn.ModuleList([ + wrapper_fn(SelfAttention(dim, causal = causal, heads = heads, dim_head = dim_head, local_heads = local_heads, local_window_size = local_window_size, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection, qkv_bias = qkv_bias)), + wrapper_fn(Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1)) + ])) + # if no need cross_attend(decoder), begin next cycle + if not cross_attend: + continue + layers.append(nn.ModuleList([ + wrapper_fn(SelfAttention(dim, heads = heads, dim_head = dim_head, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection)), + wrapper_fn(Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1)) + ])) + + execute_type = ReversibleSequence if reversible else SequentialSequence + + route_attn = ((True, False),) * depth * (2 if cross_attend else 1) # ((True, False), (True, False), (True, False), (True, False), (True, False), (True, False)) + route_context = ((False, False), (True, False)) * depth + attn_route_map = {'mask': route_attn, 'pos_emb': route_attn} + context_route_map = {'context': route_context, 'context_mask': route_context} if cross_attend else {} + self.net = execute_type(layers, args_route = {**attn_route_map, **context_route_map}) + + # keeping track of when to redraw projections for all attention layers + self.auto_check_redraw = auto_check_redraw + self.feature_redraw_interval = feature_redraw_interval + self.register_buffer('calls_since_last_redraw', torch.tensor(0)) + + def fix_projection_matrices_(self): + self.feature_redraw_interval = None + + def check_redraw_projections(self): + if not self.training: + return + + if exists(self.feature_redraw_interval) and self.calls_since_last_redraw >= self.feature_redraw_interval: + device = get_module_device(self) + + fast_attentions = find_modules(self, FastAttention) + for fast_attention in fast_attentions: + fast_attention.redraw_projection_matrix(device) + + self.calls_since_last_redraw.zero_() + return + + self.calls_since_last_redraw += 1 + + def forward(self, x, output_attentions = False, **kwargs): + if self.auto_check_redraw: + self.check_redraw_projections() + return self.net(x, output_attentions = output_attentions, **kwargs) + +class PerformerModule(nn.Module): + def __init__( + self, + max_seq_len, # max length of sequence + dim, # dim of tokens + depth, # layers + heads, # num of heads + dim_head=64, # dim of heads + local_attn_heads=0, + local_window_size=256, + causal=False, + ff_mult=4, + nb_features=None, + feature_redraw_interval=1000, + reversible=False, + ff_chunks=1, + ff_glu=False, + ff_dropout=0., + attn_dropout=0., + generalized_attention=False, + kernel_fn=nn.ReLU(), + use_scalenorm=False, + use_rezero=False, + cross_attend=False, + no_projection=False, + auto_check_redraw=True, + qkv_bias=True + ): + super(PerformerModule, self).__init__() + local_attn_heads = cast_tuple(local_attn_heads) + + self.max_seq_len = max_seq_len + + self.performer = Performer(dim, depth, heads, dim_head, local_attn_heads, local_window_size, causal, ff_mult, + nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, + kernel_fn, use_scalenorm, use_rezero, ff_glu, ff_dropout, attn_dropout, cross_attend, + no_projection, auto_check_redraw, qkv_bias) + self.norm = nn.LayerNorm(dim) + + def check_redraw_projections(self): + self.performer.check_redraw_projections() + + def fix_projection_matrices_(self): + self.performer.fix_projection_matrices_() + + def forward(self, x, output_attentions=False, **kwargs): + + b, n, _, device = *x.shape, x.device + assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' + # print('00 input b,n,device',b,n,device,x.shape) + + if output_attentions: + x, attn_weights = self.performer(x, output_attentions=output_attentions, **kwargs) + # norm and to logits + x = self.norm(x) + + return x, attn_weights + + else: + x = self.performer(x, output_attentions=output_attentions, **kwargs) + x = self.norm(x) + + return x diff --git a/src/utils/pretrainmodels/pytorchTransformer.py b/src/utils/pretrainmodels/pytorchTransformer.py new file mode 100644 index 00000000..48890d1d --- /dev/null +++ b/src/utils/pretrainmodels/pytorchTransformer.py @@ -0,0 +1,80 @@ +# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited + + +import torch +import torch.nn as nn +from .performer import Gene2VecPositionalEmbedding, RandomPositionalEmbedding + +from torch.nn import functional as F + + +class pytorchTransformerLM_mse(nn.Module): + def __init__(self, + num_tokens, + max_seq_len, + dim, + depth, + heads, + ff_mult=4, + emb_dropout=0., + g2v_position_emb=False + ): + super(pytorchTransformerLM_mse, self).__init__() + self.max_seq_len = max_seq_len + self.token_emb = nn.Embedding(num_tokens, dim) + + if g2v_position_emb: + self.pos_emb = Gene2VecPositionalEmbedding(dim, max_seq_len) + print("pos_emb Gene2Vec", self.pos_emb) + else: + self.pos_emb = RandomPositionalEmbedding(dim, max_seq_len) + print("pos_emb no ", self.pos_emb) + + self.dropout = nn.Dropout(emb_dropout) + + self.encoder_layer = nn.TransformerEncoderLayer(d_model=dim, + nhead=heads, dim_feedforward=dim*ff_mult) + self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=depth) + + self.norm = nn.LayerNorm(dim) + self.to_out = nn.Linear(dim, num_tokens) + self.to_final = nn.Linear(num_tokens, 1) + + def forward(self, x): + b, n, device = *x.shape, x.device + assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' + + # token and positional embedding + x = self.token_emb(x) + x += self.pos_emb(x) + + x = self.dropout(x) # embedding dropout + + # x = self.transformer_encoder(x) # get encodings [B, N, D] + x=torch.transpose(x, 0, 1) + x = self.transformer_encoder(x) + x = torch.transpose(x, 0, 1) + + # layernorm and to logits + x = self.norm(x) + x = self.to_out(x) # [B, N, C] + + if exists(self.to_final): + x = self.to_final(x) + return x.squeeze(2) # torch.Size([8, 13418]) + else: + return x + + return x + +def exists(val): + return val is not None + + +def _get_activation_fn(activation): + if activation == "relu": + return F.relu + elif activation == "gelu": + return F.gelu + + raise RuntimeError("activation should be relu/gelu, not {}".format(activation)) diff --git a/src/utils/pretrainmodels/reversible.py b/src/utils/pretrainmodels/reversible.py new file mode 100644 index 00000000..11fa00b7 --- /dev/null +++ b/src/utils/pretrainmodels/reversible.py @@ -0,0 +1,200 @@ +## Modeified Code from https://github.com/TencentAILabHealthcare/scBERT + +import torch +import torch.nn as nn +from operator import itemgetter +from torch.autograd.function import Function +from torch.utils.checkpoint import get_device_states, set_device_states + +# for routing arguments into the functions of the reversible layer +def route_args(router, args, depth): + routed_args = [(dict(), dict()) for _ in range(depth)] + matched_keys = [key for key in args.keys() if key in router] + + for key in matched_keys: + val = args[key] + for depth, ((f_args, g_args), routes) in enumerate(zip(routed_args, router[key])): + new_f_args, new_g_args = map(lambda route: ({key: val} if route else {}), routes) + routed_args[depth] = ({**f_args, **new_f_args}, {**g_args, **new_g_args}) + return routed_args + +# following example for saving and setting rng here https://pytorch.org/docs/stable/_modules/torch/utils/checkpoint.html +class Deterministic(nn.Module): + def __init__(self, net): + super().__init__() + self.net = net + self.cpu_state = None + self.cuda_in_fwd = None + self.gpu_devices = None + self.gpu_states = None + + def record_rng(self, *args): + self.cpu_state = torch.get_rng_state() + if torch.cuda._initialized: + self.cuda_in_fwd = True + self.gpu_devices, self.gpu_states = get_device_states(*args) + + def forward(self, *args, record_rng = False, set_rng = False, **kwargs): + if record_rng: + self.record_rng(*args) + + if not set_rng: + return self.net(*args, **kwargs) + + rng_devices = [] + if self.cuda_in_fwd: + rng_devices = self.gpu_devices + + with torch.random.fork_rng(devices=rng_devices, enabled=True): + torch.set_rng_state(self.cpu_state) + if self.cuda_in_fwd: + set_device_states(self.gpu_devices, self.gpu_states) + return self.net(*args, **kwargs) + +# heavily inspired by https://github.com/RobinBruegger/RevTorch/blob/master/revtorch/revtorch.py +# once multi-GPU is confirmed working, refactor and send PR back to source +class ReversibleBlock(nn.Module): + def __init__(self, f, g): + super().__init__() + self.f = Deterministic(f) + self.g = Deterministic(g) + + def forward(self, x, f_args = {}, g_args = {}): + x1, x2 = torch.chunk(x, 2, dim=2) + y1, y2 = None, None + + with torch.no_grad(): + y1 = x1 + self.f(x2, record_rng=self.training, **f_args) + y2 = x2 + self.g(y1, record_rng=self.training, **g_args) + + return torch.cat([y1, y2], dim=2) + + def backward_pass(self, y, dy, f_args = {}, g_args = {}): + y1, y2 = torch.chunk(y, 2, dim=2) + del y + + dy1, dy2 = torch.chunk(dy, 2, dim=2) + del dy + + with torch.enable_grad(): + y1.requires_grad = True + gy1 = self.g(y1, set_rng=True, **g_args) + torch.autograd.backward(gy1, dy2) + + with torch.no_grad(): + x2 = y2 - gy1 + del y2, gy1 + + dx1 = dy1 + y1.grad + del dy1 + y1.grad = None + + with torch.enable_grad(): + x2.requires_grad = True + fx2 = self.f(x2, set_rng=True, **f_args) + torch.autograd.backward(fx2, dx1, retain_graph=True) + + with torch.no_grad(): + x1 = y1 - fx2 + del y1, fx2 + + dx2 = dy2 + x2.grad + del dy2 + x2.grad = None + + x = torch.cat([x1, x2.detach()], dim=2) + dx = torch.cat([dx1, dx2], dim=2) + + return x, dx + +class _ReversibleFunction(Function): + @staticmethod + def forward(ctx, x, blocks, args): + ctx.args = args + for block, kwarg in zip(blocks, args): + x = block(x, **kwarg) + ctx.y = x.detach() + ctx.blocks = blocks + return x + + @staticmethod + def backward(ctx, dy): + y = ctx.y + args = ctx.args + for block, kwargs in zip(ctx.blocks[::-1], args[::-1]): + y, dy = block.backward_pass(y, dy, **kwargs) + return dy, None, None + +class SequentialSequence(nn.Module): + def __init__(self, layers, args_route = {}): + super().__init__() + assert all(len(route) == len(layers) for route in args_route.values()), 'each argument route map must have the same depth as the number of sequential layers' + self.layers = layers + self.args_route = args_route + + def forward(self, x, output_attentions = False, **kwargs): + args = route_args(self.args_route, kwargs, len(self.layers)) + layers_and_args = list(zip(self.layers, args)) + if output_attentions: + attn_weights = [] + for (f, g), (f_args, g_args) in layers_and_args: + # print(f,f_args) + if output_attentions: + x = x + f(x, output_attentions = output_attentions, **f_args)[0] + attn_weights.append(f(x, output_attentions = output_attentions, **f_args)[1].unsqueeze(0)) + else: + x = x + f(x, **f_args) + x = x + g(x, **g_args) + if output_attentions: + attn_weights = torch.transpose(torch.cat(attn_weights, dim=0), 0, 1) # the final dim is (batch, layer, head, len, len) + # attn_weights = torch.mean(attn_weights, dim=1) # the dim is (batch, head, len, len) + return x, attn_weights + else: + return x + +# GAU +class SequentialSequenceGAU(nn.Module): + def __init__(self, layers, args_route = {}): + super().__init__() + assert all(len(route) == len(layers) for route in args_route.values()), 'each argument route map must have the same depth as the number of sequential layers' + self.layers = layers + self.args_route = args_route + + def forward(self, x, output_attentions = False,ppi_edge = None, **kwargs): + args = route_args(self.args_route, kwargs, len(self.layers)) + layers_and_args = list(zip(self.layers, args)) + + if output_attentions: + attn_weights = [] + for f ,(f_args,g_args) in layers_and_args: + f = f[0] + # print(f,f_args) + if output_attentions: + tmpout = f(x, output_attentions = output_attentions,ppi_edge=ppi_edge, **f_args) + x = x + tmpout[0] + attn_weights.append(tmpout[1].unsqueeze(0)) + else: + x = x + f(x,ppi_edge=ppi_edge, **f_args) + if output_attentions: + attn_weights = torch.transpose(torch.cat(attn_weights, dim=0), 0, 1) # the final dim is (batch, layer, head, len, len) + # attn_weights = torch.mean(attn_weights, dim=1) # the dim is (batch, head, len, len) + return x, attn_weights + else: + return x + + +class ReversibleSequence(nn.Module): + def __init__(self, blocks, args_route = {}): + super().__init__() + self.args_route = args_route + self.blocks = nn.ModuleList([ReversibleBlock(f=f, g=g) for f, g in blocks]) + + def forward(self, x, **kwargs): + x = torch.cat([x, x], dim=-1) + + blocks = self.blocks + args = route_args(self.args_route, kwargs, len(blocks)) + args = list(map(lambda x: {'f_args': x[0], 'g_args': x[1]}, args)) + + out = _ReversibleFunction.apply(x, blocks, args) + return torch.stack(out.chunk(2, dim=-1)).sum(dim=0) diff --git a/src/utils/pretrainmodels/select_model.py b/src/utils/pretrainmodels/select_model.py new file mode 100644 index 00000000..d2a781e6 --- /dev/null +++ b/src/utils/pretrainmodels/select_model.py @@ -0,0 +1,61 @@ +# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited + + +from .transformer import pytorchTransformerModule +from .performer import PerformerModule +from .mae_autobin import MaeAutobin + +def select_module(config, sub_config, module_name): + if module_name == 'performer': + return PerformerModule( + max_seq_len=config['seq_len'], + dim=sub_config['hidden_dim'], + depth=sub_config['depth'], + heads=sub_config['heads'], + dim_head=sub_config['dim_head'], + ff_dropout=sub_config.get('ff_dropout',0.0), + attn_dropout=sub_config.get('attn_dropout',0.0) + ) + elif module_name == 'transformer': + return pytorchTransformerModule( + max_seq_len=config['seq_len'], + dim=sub_config['hidden_dim'], + depth=sub_config['depth'], + heads=sub_config['heads'] + ) + else: + print('module type error') + exit(0) + +def select_model(config): + if config["model"] == "mae_autobin": + encoder_config =config['encoder'] + decoder_config = config['decoder'] + encoder = select_module(config, encoder_config, config['encoder']['module_type']) + decoder = select_module(config, decoder_config, config['decoder']['module_type']) + model = MaeAutobin( + num_tokens=config['n_class'], + max_seq_len=config['seq_len'], + embed_dim=config['encoder']['hidden_dim'], + decoder_embed_dim=config['decoder']['hidden_dim'], + bin_alpha = config['bin_alpha'], + bin_num = config['bin_num'], + pad_token_id = config['pad_token_id'], + mask_token_id = config['mask_token_id'], + ) + model.encoder = encoder + model.decoder = decoder + else: + raise NotImplementedError("Unknown model type!") + return model + +def get_sub_config(config, target): + """ + 获取 包含 target 的 config + """ + sub_config = {} + for k in config.keys(): + if target in k: + tmp_name = k.replace(target + '_', '') + sub_config[tmp_name] = config[k] + return sub_config diff --git a/src/utils/pretrainmodels/transformer.py b/src/utils/pretrainmodels/transformer.py new file mode 100644 index 00000000..f7ee5b9e --- /dev/null +++ b/src/utils/pretrainmodels/transformer.py @@ -0,0 +1,43 @@ +# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited + + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class pytorchTransformerModule(nn.Module): + def __init__(self, + max_seq_len, + dim, + depth, + heads, + ff_mult=4, + norm_first=False, + ): + super(pytorchTransformerModule, self).__init__() + + self.max_seq_len = max_seq_len + self.depth = depth + layers = [] + for i in range(depth): + layers.append(nn.TransformerEncoderLayer(d_model=dim, nhead=heads, + dim_feedforward=dim * ff_mult, + batch_first=True, + norm_first=norm_first, + #activation="gelu", + )) + + self.transformer_encoder = nn.ModuleList(layers) + self.norm = nn.LayerNorm(dim) + + def forward(self, x, padding_mask): + b, n, _, device = *x.shape, x.device + assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' + + # x get encodings [B, N, D] , batch_first is True + for mod in self.transformer_encoder: + x = mod(x, src_key_padding_mask=padding_mask) # , src_mask=mask, src_key_padding_mask=src_key_padding_mask) + # x = self.transformer_encoder(x) + x = self.norm(x) + + return x From 6156a77e2c1113816312eff0b66ef374dcadd748 Mon Sep 17 00:00:00 2001 From: HelloWorldLTY Date: Sat, 14 Dec 2024 10:28:03 -0500 Subject: [PATCH 02/16] update cellplm --- CHANGELOG.md | 13 +++---------- src/methods/cellplm/config.vsh.yaml | 23 +++++++++++++---------- src/methods/cellplm/script.py | 18 ++++++++++-------- src/methods/scfoundation/config.vsh.yaml | 22 ++++++++++++---------- src/methods/scfoundation/script.py | 18 ++++++++++-------- 5 files changed, 48 insertions(+), 46 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2ba5cf94..603ab1bf 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,21 +6,14 @@ ## NEW FUNCTIONALITY -* Added `control_methods/true_labels` component (PR #5). +* Added `methods/cellplm` component. -* Added `methods/logistic_regression` component (PR #5). +* Added `methods/scfoundation` component. -* Added `metrics/accuracy` component (PR #5). ## MAJOR CHANGES -* Updated `api` files (PR #5). +* Updated scripts. -* Updated configs, components and CI to the latest Viash version (PR #8). -## MINOR CHANGES - -* Updated `README.md` (PR #5). - -## BUGFIXES diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index 54390a1a..9878cbce 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -18,16 +18,18 @@ summary: "A foundation model pre-trained with cells as tokens." description: | CellPLM is a pre-trained language model specifically designed for single-cell analysis that leverages the principles of natural language processing (NLP) to understand and process single-cell gene expression data. references: - doi: - - https://openreview.net/forum?id=BKXvPDekud -# bibtex: -# - | -# @article{foo, -# title={Foo}, -# author={Bar}, -# journal={Baz}, -# year={2024} -# } + # doi: + # - https://openreview.net/forum?id=BKXvPDekud + bibtex: + - | + @inproceedings{ + wen2024cellplm, + title={Cell{PLM}: Pre-training of Cell Language Model Beyond Single Cells}, + author={Hongzhi Wen and Wenzhuo Tang and Xinnan Dai and Jiayuan Ding and Wei Jin and Yuying Xie and Jiliang Tang}, + booktitle={The Twelfth International Conference on Learning Representations}, + year={2024}, + url={https://openreview.net/forum?id=BKXvPDekud} + } links: # URL to the documentation for this method (required). documentation: https://github.com/OmicsML/CellPLM @@ -62,6 +64,7 @@ engines: setup: - type: python pypi: + - torch==1.13.0 - gdown - scgpt # Install from PyPI to get dependencies - cellplm diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index d9e8d3d9..58c8cc3f 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -3,7 +3,6 @@ import scanpy as sc import anndata as ad import gdown -import cellplm import torch import warnings @@ -18,11 +17,6 @@ # Note: this section is auto-generated by viash at runtime. To edit it, make changes # in config.vsh.yaml and then run `viash config inject config.vsh.yaml`. -drive_path = f"https://drive.google.com/drive/folders/1C2fVNEKX3plHnagaTwpuPW5tpwv1up9G?usp=sharing" -model_dir = tempfile.TemporaryDirectory() -print(f"Downloading from '{drive_path}'", flush=True) -gdown.download_folder(drive_path, output=model_dir.name, quiet=True) -print(f"Model directory: '{model_dir.name}'", flush=True) set_seed(24) PRETRAIN_VERSION = '20231027_85M' @@ -36,7 +30,7 @@ 'name': 'cellplm' } ## VIASH END -print(f"====== scGPT version {PRETRAIN_VERSION} ======", flush=True) +# print(f"====== scGPT version {PRETRAIN_VERSION} ======", flush=True) sys.path.append(meta["resources_dir"]) from read_anndata_partial import read_anndata @@ -61,9 +55,17 @@ print('Train model', flush=True) # ... train model ... +drive_path = f"https://drive.google.com/drive/folders/1C2fVNEKX3plHnagaTwpuPW5tpwv1up9G?usp=sharing" +model_dir = tempfile.TemporaryDirectory() +print(f"Downloading from '{drive_path}'", flush=True) +gdown.download_folder(drive_path, output=model_dir.name, quiet=True) +print(f"Model directory: '{model_dir.name}'", flush=True) + +PRETRAIN_VERSION = '20231027_85M' pipeline = CellEmbeddingPipeline(pretrain_prefix=PRETRAIN_VERSION, # Specify the pretrain checkpoint to load - pretrain_directory=model_dir) + pretrain_directory=model_dir.name) +# DEVICE ='cpu' embedding = pipeline.predict(adata, # An AnnData object device=DEVICE) # Specify a gpu or cpu for model inference diff --git a/src/methods/scfoundation/config.vsh.yaml b/src/methods/scfoundation/config.vsh.yaml index 0d6279f9..84435bbd 100644 --- a/src/methods/scfoundation/config.vsh.yaml +++ b/src/methods/scfoundation/config.vsh.yaml @@ -18,16 +18,18 @@ summary: "A foundation model pre-trained with downsampling pre-training method." description: | scFoundation is a large-scale foundation model designed specifically for single-cell genomics analysis. It leverages self-supervised learning strategies to understand the complex patterns in single-cell gene expression data, enabling various downstream tasks through transfer learning and fine-tuning approaches. references: - doi: - - https://www.nature.com/articles/s41592-024-02305-7 -# bibtex: -# - | -# @article{foo, -# title={Foo}, -# author={Bar}, -# journal={Baz}, -# year={2024} -# } + # doi: + # - https://www.nature.com/articles/s41592-024-02305-7 + bibtex: + - | + @article{hao2024large, + title={Large-scale foundation model on single-cell transcriptomics}, + author={Hao, Minsheng and Gong, Jing and Zeng, Xin and Liu, Chiming and Guo, Yucheng and Cheng, Xingyi and Wang, Taifeng and Ma, Jianzhu and Zhang, Xuegong and Song, Le}, + journal={Nature Methods}, + pages={1--11}, + year={2024}, + publisher={Nature Publishing Group US New York} + } links: # URL to the documentation for this method (required). documentation: https://github.com/biomap-research/scFoundation diff --git a/src/methods/scfoundation/script.py b/src/methods/scfoundation/script.py index 40f43978..aec2575d 100644 --- a/src/methods/scfoundation/script.py +++ b/src/methods/scfoundation/script.py @@ -3,7 +3,6 @@ import scanpy as sc import anndata as ad import gdown -import cellplm import torch import warnings @@ -13,8 +12,6 @@ import numpy as np import anndata as ad -from get_embeddings import * - ## VIASH START # Note: this section is auto-generated by viash at runtime. To edit it, make changes # in config.vsh.yaml and then run `viash config inject config.vsh.yaml`. @@ -27,10 +24,7 @@ 'input': 'resources_test/.../input.h5ad', 'output': 'output.h5ad' } -meta = { - 'name': 'scfoundation' -} - +meta = {"name": "scfoundation"} sys.path.append(meta["resources_dir"]) from read_anndata_partial import read_anndata @@ -47,7 +41,15 @@ print('Train model', flush=True) # ... train model ... -embedding = main_get(data=adata, model_path=model_path) +drive_path = f"wait to fill" +model_dir = tempfile.TemporaryDirectory() +print(f"Downloading from '{drive_path}'", flush=True) +gdown.download_folder(drive_path, output=model_dir.name, quiet=True) +print(f"Model directory: '{model_dir.name}'", flush=True) + + +from get_embedding import main_get +embedding = main_get(data=adata, model_path=model_dir.name + '/' + model_path) print('Generate predictions', flush=True) # ... generate predictions ... From f479b8563827acce2cdf18ab2cec845b060031a5 Mon Sep 17 00:00:00 2001 From: HelloWorldLTY Date: Tue, 17 Dec 2024 14:52:12 -0500 Subject: [PATCH 03/16] update cellplm and scf --- src/methods/cellplm/config.vsh.yaml | 2 +- src/methods/scfoundation/config.vsh.yaml | 2 +- src/methods/scfoundation/script.py | 5 +++++ 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index 9878cbce..5d285c7a 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -3,7 +3,7 @@ # - The input/output files # - Common parameters # - A unit test -__merge__: ../../api/comp_method.yaml +__merge__: ../../api/base_method.yaml # A unique identifier for your component (required). # Can contain only lowercase letters or underscores. diff --git a/src/methods/scfoundation/config.vsh.yaml b/src/methods/scfoundation/config.vsh.yaml index 84435bbd..275ca100 100644 --- a/src/methods/scfoundation/config.vsh.yaml +++ b/src/methods/scfoundation/config.vsh.yaml @@ -3,7 +3,7 @@ # - The input/output files # - Common parameters # - A unit test -__merge__: ../../api/comp_method.yaml +__merge__: ../../api/base_method.yaml # A unique identifier for your component (required). # Can contain only lowercase letters or underscores. diff --git a/src/methods/scfoundation/script.py b/src/methods/scfoundation/script.py index aec2575d..840ce71a 100644 --- a/src/methods/scfoundation/script.py +++ b/src/methods/scfoundation/script.py @@ -25,6 +25,11 @@ 'output': 'output.h5ad' } meta = {"name": "scfoundation"} + +## VIASH END + + + sys.path.append(meta["resources_dir"]) from read_anndata_partial import read_anndata From 62bbea8b30e80a620f8295724c94c681aab81cb8 Mon Sep 17 00:00:00 2001 From: HelloWorldLTY Date: Thu, 19 Dec 2024 12:38:55 -0500 Subject: [PATCH 04/16] only cellplm --- src/methods/scfoundation/config.vsh.yaml | 80 - src/methods/scfoundation/script.py | 77 - src/utils/OS_scRNA_gene_index.19264.tsv | 19265 ---------------- src/utils/get_embedding.py | 259 - src/utils/load.py | 198 - src/utils/pretrainmodels/__init__.py | 3 - src/utils/pretrainmodels/mae_autobin.py | 162 - src/utils/pretrainmodels/performer.py | 638 - .../pretrainmodels/pytorchTransformer.py | 80 - src/utils/pretrainmodels/reversible.py | 200 - src/utils/pretrainmodels/select_model.py | 61 - src/utils/pretrainmodels/transformer.py | 43 - 12 files changed, 21066 deletions(-) delete mode 100644 src/methods/scfoundation/config.vsh.yaml delete mode 100644 src/methods/scfoundation/script.py delete mode 100644 src/utils/OS_scRNA_gene_index.19264.tsv delete mode 100644 src/utils/get_embedding.py delete mode 100644 src/utils/load.py delete mode 100644 src/utils/pretrainmodels/__init__.py delete mode 100644 src/utils/pretrainmodels/mae_autobin.py delete mode 100644 src/utils/pretrainmodels/performer.py delete mode 100644 src/utils/pretrainmodels/pytorchTransformer.py delete mode 100644 src/utils/pretrainmodels/reversible.py delete mode 100644 src/utils/pretrainmodels/select_model.py delete mode 100644 src/utils/pretrainmodels/transformer.py diff --git a/src/methods/scfoundation/config.vsh.yaml b/src/methods/scfoundation/config.vsh.yaml deleted file mode 100644 index 275ca100..00000000 --- a/src/methods/scfoundation/config.vsh.yaml +++ /dev/null @@ -1,80 +0,0 @@ -# The API specifies which type of component this is. -# It contains specifications for: -# - The input/output files -# - Common parameters -# - A unit test -__merge__: ../../api/base_method.yaml - -# A unique identifier for your component (required). -# Can contain only lowercase letters or underscores. -name: scfoundation -# A relatively short label, used when rendering visualisations (required) -label: scfoundation -# A one sentence summary of how this method works (required). Used when -# rendering summary tables. -summary: "A foundation model pre-trained with downsampling pre-training method." -# A multi-line description of how this component works (required). Used -# when rendering reference documentation. -description: | - scFoundation is a large-scale foundation model designed specifically for single-cell genomics analysis. It leverages self-supervised learning strategies to understand the complex patterns in single-cell gene expression data, enabling various downstream tasks through transfer learning and fine-tuning approaches. -references: - # doi: - # - https://www.nature.com/articles/s41592-024-02305-7 - bibtex: - - | - @article{hao2024large, - title={Large-scale foundation model on single-cell transcriptomics}, - author={Hao, Minsheng and Gong, Jing and Zeng, Xin and Liu, Chiming and Guo, Yucheng and Cheng, Xingyi and Wang, Taifeng and Ma, Jianzhu and Zhang, Xuegong and Song, Le}, - journal={Nature Methods}, - pages={1--11}, - year={2024}, - publisher={Nature Publishing Group US New York} - } -links: - # URL to the documentation for this method (required). - documentation: https://github.com/biomap-research/scFoundation - # URL to the code repository for this method (required). - repository: https://github.com/biomap-research/scFoundation - - - -info: - method_types: [embedding] - preferred_normalization: counts - -# arguments: -# - name: --model -# type: string -# description: String giving the scGPT model to use -# choices: ["cellplm"] -# default: "cellplm" -# - name: --n_hvg -# type: integer -# default: 3000 -# description: Number of highly variable genes to use. - -resources: - - type: python_script - path: script.py - - path: /src/utils/read_anndata_partial.py - - path: /src/utils/load.py - - path: /src/utils/get_embedding.py - - - type: file - path: /src/utils/OS_scRNA_gene_index.19264.tsv - -engines: - - type: docker - image: openproblems/base_pytorch_nvidia:1.0.0 - # TODO: Try to find working installation of flash attention (flash-attn<1.0.5) - setup: - - type: python - pypi: - - gdown - - scanpy - -runners: - - type: executable - - type: nextflow - directives: - label: [midtime, midmem, midcpu, gpu] diff --git a/src/methods/scfoundation/script.py b/src/methods/scfoundation/script.py deleted file mode 100644 index 840ce71a..00000000 --- a/src/methods/scfoundation/script.py +++ /dev/null @@ -1,77 +0,0 @@ -import sys -import tempfile -import scanpy as sc -import anndata as ad -import gdown -import torch - -import warnings -warnings.filterwarnings("ignore") - - -import numpy as np -import anndata as ad - -## VIASH START -# Note: this section is auto-generated by viash at runtime. To edit it, make changes -# in config.vsh.yaml and then run `viash config inject config.vsh.yaml`. - -model_path = "models.ckpt" - -DEVICE = "cuda" if torch.cuda.is_available() else "cpu" - -par = { - 'input': 'resources_test/.../input.h5ad', - 'output': 'output.h5ad' -} -meta = {"name": "scfoundation"} - -## VIASH END - - - -sys.path.append(meta["resources_dir"]) -from read_anndata_partial import read_anndata - -print("\n>>> Reading input files...", flush=True) -print(f"Input H5AD file: '{par['input']}'", flush=True) -adata = read_anndata(par["input"], X="layers/counts", obs="obs", var="var", uns="uns") - - -print(adata, flush=True) - -print('Preprocess data', flush=True) -# ... preprocessing ... - -print('Train model', flush=True) -# ... train model ... - -drive_path = f"wait to fill" -model_dir = tempfile.TemporaryDirectory() -print(f"Downloading from '{drive_path}'", flush=True) -gdown.download_folder(drive_path, output=model_dir.name, quiet=True) -print(f"Model directory: '{model_dir.name}'", flush=True) - - -from get_embedding import main_get -embedding = main_get(data=adata, model_path=model_dir.name + '/' + model_path) - -print('Generate predictions', flush=True) -# ... generate predictions ... - -output = ad.AnnData( - obs=adata.obs[[]], - var=adata.var[[]], - obsm={ - "X_emb": embedding, - }, - uns={ - "dataset_id": adata.uns["dataset_id"], - "normalization_id": adata.uns["normalization_id"], - "method_id": meta["name"], - }, -) -print(output) - -output.write_h5ad(par['output'], compression='gzip') - diff --git a/src/utils/OS_scRNA_gene_index.19264.tsv b/src/utils/OS_scRNA_gene_index.19264.tsv deleted file mode 100644 index e02ea022..00000000 --- a/src/utils/OS_scRNA_gene_index.19264.tsv +++ /dev/null @@ -1,19265 +0,0 @@ -gene_name index -A1BG 0 -A1CF 1 -A2M 2 -A2ML1 3 -A3GALT2 4 -A4GALT 5 -A4GNT 6 -AAAS 7 -AACS 8 -AADAC 9 -AADACL2 10 -AADACL3 11 -AADACL4 12 -AADAT 13 -AAGAB 14 -AAK1 15 -AAMDC 16 -AAMP 17 -AANAT 18 -AAR2 19 -AARD 20 -AARS1 21 -AARS2 22 -AARSD1 23 -AASDH 24 -AASDHPPT 25 -AASS 26 -AATF 27 -AATK 28 -ABAT 29 -ABCA1 30 -ABCA10 31 -ABCA12 32 -ABCA13 33 -ABCA2 34 -ABCA3 35 -ABCA4 36 -ABCA5 37 -ABCA6 38 -ABCA7 39 -ABCA8 40 -ABCA9 41 -ABCB1 42 -ABCB10 43 -ABCB11 44 -ABCB4 45 -ABCB5 46 -ABCB6 47 -ABCB7 48 -ABCB8 49 -ABCB9 50 -ABCC1 51 -ABCC10 52 -ABCC11 53 -ABCC12 54 -ABCC2 55 -ABCC3 56 -ABCC4 57 -ABCC5 58 -ABCC6 59 -ABCC8 60 -ABCC9 61 -ABCD1 62 -ABCD2 63 -ABCD3 64 -ABCD4 65 -ABCE1 66 -ABCF1 67 -ABCF2 68 -ABCF3 69 -ABCG1 70 -ABCG2 71 -ABCG4 72 -ABCG5 73 -ABCG8 74 -ABHD1 75 -ABHD10 76 -ABHD11 77 -ABHD12 78 -ABHD12B 79 -ABHD13 80 -ABHD14A 81 -ABHD14B 82 -ABHD15 83 -ABHD16A 84 -ABHD16B 85 -ABHD17A 86 -ABHD17B 87 -ABHD17C 88 -ABHD18 89 -ABHD2 90 -ABHD3 91 -ABHD4 92 -ABHD5 93 -ABHD6 94 -ABHD8 95 -ABI1 96 -ABI2 97 -ABI3 98 -ABI3BP 99 -ABITRAM 100 -ABL1 101 -ABL2 102 -ABLIM1 103 -ABLIM2 104 -ABLIM3 105 -ABO 106 -ABR 107 -ABRA 108 -ABRACL 109 -ABRAXAS1 110 -ABRAXAS2 111 -ABT1 112 -ABTB1 113 -ABTB2 114 -ACAA1 115 -ACAA2 116 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208 -ACTL6A 209 -ACTL6B 210 -ACTL7A 211 -ACTL7B 212 -ACTL8 213 -ACTL9 214 -ACTN1 215 -ACTN2 216 -ACTN3 217 -ACTN4 218 -ACTR10 219 -ACTR1A 220 -ACTR1B 221 -ACTR2 222 -ACTR3 223 -ACTR3B 224 -ACTR3C 225 -ACTR5 226 -ACTR6 227 -ACTR8 228 -ACTRT1 229 -ACTRT2 230 -ACTRT3 231 -ACVR1 232 -ACVR1B 233 -ACVR1C 234 -ACVR2A 235 -ACVR2B 236 -ACVRL1 237 -ACY1 238 -ACY3 239 -ACYP1 240 -ACYP2 241 -ADA 242 -ADA2 243 -ADAD1 244 -ADAD2 245 -ADAL 246 -ADAM10 247 -ADAM11 248 -ADAM12 249 -ADAM15 250 -ADAM17 251 -ADAM18 252 -ADAM19 253 -ADAM2 254 -ADAM20 255 -ADAM21 256 -ADAM22 257 -ADAM23 258 -ADAM28 259 -ADAM29 260 -ADAM30 261 -ADAM32 262 -ADAM33 263 -ADAM7 264 -ADAM8 265 -ADAM9 266 -ADAMDEC1 267 -ADAMTS1 268 -ADAMTS10 269 -ADAMTS12 270 -ADAMTS13 271 -ADAMTS14 272 -ADAMTS15 273 -ADAMTS16 274 -ADAMTS17 275 -ADAMTS18 276 -ADAMTS19 277 -ADAMTS2 278 -ADAMTS20 279 -ADAMTS3 280 -ADAMTS4 281 -ADAMTS5 282 -ADAMTS6 283 -ADAMTS7 284 -ADAMTS8 285 -ADAMTS9 286 -ADAMTSL1 287 -ADAMTSL2 288 -ADAMTSL3 289 -ADAMTSL4 290 -ADAMTSL5 291 -ADAP1 292 -ADAP2 293 -ADAR 294 -ADARB1 295 -ADARB2 296 -ADAT1 297 -ADAT2 298 -ADAT3 299 -ADCK1 300 -ADCK2 301 -ADCK5 302 -ADCY1 303 -ADCY10 304 -ADCY2 305 -ADCY3 306 -ADCY4 307 -ADCY5 308 -ADCY6 309 -ADCY7 310 -ADCY8 311 -ADCY9 312 -ADCYAP1 313 -ADCYAP1R1 314 -ADD1 315 -ADD2 316 -ADD3 317 -ADGB 318 -ADGRA1 319 -ADGRA2 320 -ADGRA3 321 -ADGRB1 322 -ADGRB2 323 -ADGRB3 324 -ADGRD1 325 -ADGRD2 326 -ADGRE1 327 -ADGRE2 328 -ADGRE3 329 -ADGRE5 330 -ADGRF1 331 -ADGRF2 332 -ADGRF3 333 -ADGRF4 334 -ADGRF5 335 -ADGRG1 336 -ADGRG2 337 -ADGRG3 338 -ADGRG4 339 -ADGRG5 340 -ADGRG6 341 -ADGRG7 342 -ADGRL1 343 -ADGRL2 344 -ADGRL3 345 -ADGRL4 346 -ADGRV1 347 -ADH1A 348 -ADH1B 349 -ADH1C 350 -ADH4 351 -ADH5 352 -ADH6 353 -ADH7 354 -ADHFE1 355 -ADI1 356 -ADIG 357 -ADIPOQ 358 -ADIPOR1 359 -ADIPOR2 360 -ADIRF 361 -ADK 362 -ADM 363 -ADM2 364 -ADM5 365 -ADNP 366 -ADNP2 367 -ADO 368 -ADORA1 369 -ADORA2A 370 -ADORA2B 371 -ADORA3 372 -ADPGK 373 -ADPRH 374 -ADPRHL1 375 -ADPRM 376 -ADPRS 377 -ADRA1A 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558 -ALG10B 559 -ALG11 560 -ALG12 561 -ALG13 562 -ALG14 563 -ALG1L 564 -ALG1L2 565 -ALG2 566 -ALG3 567 -ALG5 568 -ALG6 569 -ALG8 570 -ALG9 571 -ALK 572 -ALKAL1 573 -ALKAL2 574 -ALKBH1 575 -ALKBH2 576 -ALKBH3 577 -ALKBH4 578 -ALKBH5 579 -ALKBH6 580 -ALKBH7 581 -ALKBH8 582 -ALLC 583 -ALMS1 584 -ALOX12 585 -ALOX12B 586 -ALOX15 587 -ALOX15B 588 -ALOX5 589 -ALOX5AP 590 -ALOXE3 591 -ALPG 592 -ALPI 593 -ALPK1 594 -ALPK2 595 -ALPK3 596 -ALPL 597 -ALPP 598 -ALS2 599 -ALS2CL 600 -ALX1 601 -ALX3 602 -ALX4 603 -ALYREF 604 -AMACR 605 -AMBN 606 -AMBP 607 -AMBRA1 608 -AMDHD1 609 -AMDHD2 610 -AMD1 611 -AMELX 612 -AMELY 613 -AMER1 614 -AMER2 615 -AMER3 616 -AMFR 617 -AMH 618 -AMHR2 619 -AMIGO1 620 -AMIGO2 621 -AMIGO3 622 -AMMECR1 623 -AMMECR1L 624 -AMN 625 -AMN1 626 -AMOT 627 -AMOTL1 628 -AMOTL2 629 -AMPD1 630 -AMPD2 631 -AMPD3 632 -AMPH 633 -AMT 634 -AMTN 635 -AMY1A 636 -AMY1B 637 -AMY1C 638 -AMY2A 639 -AMY2B 640 -AMZ1 641 -AMZ2 642 -ANAPC1 643 -ANAPC10 644 -ANAPC11 645 -ANAPC13 646 -ANAPC15 647 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814 -AP4S1 815 -AP5B1 816 -AP5M1 817 -AP5S1 818 -AP5Z1 819 -APAF1 820 -APBA1 821 -APBA2 822 -APBA3 823 -APBB1 824 -APBB1IP 825 -APBB2 826 -APBB3 827 -APC 828 -APC2 829 -APCDD1 830 -APCDD1L 831 -APCS 832 -APEH 833 -APELA 834 -APEX1 835 -APEX2 836 -APH1A 837 -APH1B 838 -API5 839 -APIP 840 -APLF 841 -APLN 842 -APLNR 843 -APLP1 844 -APLP2 845 -APMAP 846 -APOA1 847 -APOA2 848 -APOA4 849 -APOA5 850 -APOB 851 -APOBEC1 852 -APOBEC2 853 -APOBEC3A 854 -APOBEC3B 855 -APOBEC3C 856 -APOBEC3D 857 -APOBEC3F 858 -APOBEC3G 859 -APOBEC3H 860 -APOBEC4 861 -APOBR 862 -APOC1 863 -APOC2 864 -APOC3 865 -APOC4 866 -APOD 867 -APOE 868 -APOF 869 -APOH 870 -APOL1 871 -APOL2 872 -APOL3 873 -APOL4 874 -APOL5 875 -APOL6 876 -APOLD1 877 -APOM 878 -APOO 879 -APOOL 880 -APP 881 -APPBP2 882 -APPL1 883 -APPL2 884 -APRT 885 -APTX 886 -AQP1 887 -AQP10 888 -AQP11 889 -AQP12A 890 -AQP12B 891 -AQP2 892 -AQP3 893 -AQP4 894 -AQP5 895 -AQP6 896 -AQP7 897 -AQP7B 898 -AQP8 899 -AQP9 900 -AQR 901 -AR 902 -ARAF 903 -ARAP1 904 -ARAP2 905 -ARAP3 906 -ARC 907 -ARCN1 908 -AREG 909 -AREL1 910 -ARF1 911 -ARF3 912 -ARF4 913 -ARF5 914 -ARF6 915 -ARFGAP1 916 -ARFGAP2 917 -ARFGAP3 918 -ARFGEF1 919 -ARFGEF2 920 -ARFGEF3 921 -ARFIP1 922 -ARFIP2 923 -ARFRP1 924 -ARG1 925 -ARG2 926 -ARGFX 927 -ARGLU1 928 -ARHGAP1 929 -ARHGAP10 930 -ARHGAP11A 931 -ARHGAP11B 932 -ARHGAP12 933 -ARHGAP15 934 -ARHGAP17 935 -ARHGAP18 936 -ARHGAP19 937 -ARHGAP20 938 -ARHGAP21 939 -ARHGAP22 940 -ARHGAP23 941 -ARHGAP24 942 -ARHGAP25 943 -ARHGAP26 944 -ARHGAP27 945 -ARHGAP28 946 -ARHGAP29 947 -ARHGAP30 948 -ARHGAP31 949 -ARHGAP32 950 -ARHGAP33 951 -ARHGAP35 952 -ARHGAP36 953 -ARHGAP39 954 -ARHGAP4 955 -ARHGAP40 956 -ARHGAP42 957 -ARHGAP44 958 -ARHGAP45 959 -ARHGAP5 960 -ARHGAP6 961 -ARHGAP8 962 -ARHGAP9 963 -ARHGDIA 964 -ARHGDIB 965 -ARHGDIG 966 -ARHGEF1 967 -ARHGEF10 968 -ARHGEF10L 969 -ARHGEF11 970 -ARHGEF12 971 -ARHGEF15 972 -ARHGEF16 973 -ARHGEF17 974 -ARHGEF18 975 -ARHGEF19 976 -ARHGEF2 977 -ARHGEF25 978 -ARHGEF26 979 -ARHGEF28 980 -ARHGEF3 981 -ARHGEF33 982 -ARHGEF35 983 -ARHGEF37 984 -ARHGEF38 985 -ARHGEF39 986 -ARHGEF4 987 -ARHGEF40 988 -ARHGEF5 989 -ARHGEF6 990 -ARHGEF7 991 -ARHGEF9 992 -ARID1A 993 -ARID1B 994 -ARID2 995 -ARID3A 996 -ARID3B 997 -ARID3C 998 -ARID4A 999 -ARID4B 1000 -ARID5A 1001 -ARID5B 1002 -ARIH1 1003 -ARIH2 1004 -ARL13A 1005 -ARL13B 1006 -ARL14EP 1007 -ARL14EPL 1008 -ARL17A 1009 -ARL17B 1010 -ARL2BP 1011 -ARL4A 1012 -ARL4C 1013 -ARL4D 1014 -ARL5A 1015 -ARL5B 1016 -ARL5C 1017 -ARL6IP1 1018 -ARL6IP4 1019 -ARL6IP5 1020 -ARL6IP6 1021 -ARL8A 1022 -ARL8B 1023 -ARL1 1024 -ARL10 1025 -ARL11 1026 -ARL14 1027 -ARL15 1028 -ARL16 1029 -ARL2 1030 -ARL3 1031 -ARL6 1032 -ARL9 1033 -ARMC1 1034 -ARMC10 1035 -ARMC12 1036 -ARMC2 1037 -ARMC3 1038 -ARMC5 1039 -ARMC6 1040 -ARMC7 1041 -ARMC8 1042 -ARMC9 1043 -ARMCX1 1044 -ARMCX2 1045 -ARMCX3 1046 -ARMCX4 1047 -ARMCX5 1048 -ARMCX6 1049 -ARMH1 1050 -ARMH2 1051 -ARMH3 1052 -ARMH4 1053 -ARMS2 1054 -ARMT1 1055 -ARNT 1056 -ARNT2 1057 -ARNTL 1058 -ARNTL2 1059 -ARPC1A 1060 -ARPC1B 1061 -ARPC2 1062 -ARPC3 1063 -ARPC4 1064 -ARPC5 1065 -ARPC5L 1066 -ARPIN 1067 -ARPP19 1068 -ARPP21 1069 -ARR3 1070 -ARRB1 1071 -ARRB2 1072 -ARRDC1 1073 -ARRDC2 1074 -ARRDC3 1075 -ARRDC4 1076 -ARRDC5 1077 -ARSA 1078 -ARSB 1079 -ARSD 1080 -ARSF 1081 -ARSG 1082 -ARSH 1083 -ARSI 1084 -ARSJ 1085 -ARSK 1086 -ARSL 1087 -ART1 1088 -ART3 1089 -ART4 1090 -ART5 1091 -ARTN 1092 -ARV1 1093 -ARVCF 1094 -ARX 1095 -AS3MT 1096 -ASAH1 1097 -ASAH2 1098 -ASAH2B 1099 -ASAP1 1100 -ASAP2 1101 -ASAP3 1102 -ASB1 1103 -ASB10 1104 -ASB11 1105 -ASB12 1106 -ASB13 1107 -ASB14 1108 -ASB15 1109 -ASB16 1110 -ASB17 1111 -ASB18 1112 -ASB2 1113 -ASB3 1114 -ASB4 1115 -ASB5 1116 -ASB6 1117 -ASB7 1118 -ASB8 1119 -ASB9 1120 -ASCC1 1121 -ASCC2 1122 -ASCC3 1123 -ASCL1 1124 -ASCL2 1125 -ASCL3 1126 -ASCL4 1127 -ASCL5 1128 -ASDURF 1129 -ASF1A 1130 -ASF1B 1131 -ASGR1 1132 -ASGR2 1133 -ASH1L 1134 -ASH2L 1135 -ASIC1 1136 -ASIC2 1137 -ASIC3 1138 -ASIC4 1139 -ASIC5 1140 -ASIP 1141 -ASL 1142 -ASMT 1143 -ASMTL 1144 -ASNS 1145 -ASNSD1 1146 -ASPA 1147 -ASPDH 1148 -ASPG 1149 -ASPH 1150 -ASPHD1 1151 -ASPHD2 1152 -ASPM 1153 -ASPN 1154 -ASPRV1 1155 -ASPSCR1 1156 -ASRGL1 1157 -ASS1 1158 -ASTE1 1159 -ASTL 1160 -ASTN1 1161 -ASTN2 1162 -ASXL1 1163 -ASXL2 1164 -ASXL3 1165 -ASZ1 1166 -ATAD1 1167 -ATAD2 1168 -ATAD2B 1169 -ATAD3A 1170 -ATAD3B 1171 -ATAD3C 1172 -ATAD5 1173 -ATAT1 1174 -ATCAY 1175 -ATE1 1176 -ATF1 1177 -ATF2 1178 -ATF3 1179 -ATF4 1180 -ATF5 1181 -ATF6 1182 -ATF6B 1183 -ATF7 1184 -ATF7IP 1185 -ATF7IP2 1186 -ATG10 1187 -ATG101 1188 -ATG12 1189 -ATG13 1190 -ATG14 1191 -ATG16L1 1192 -ATG16L2 1193 -ATG2A 1194 -ATG2B 1195 -ATG3 1196 -ATG4A 1197 -ATG4B 1198 -ATG4C 1199 -ATG4D 1200 -ATG5 1201 -ATG7 1202 -ATG9A 1203 -ATG9B 1204 -ATIC 1205 -ATL1 1206 -ATL2 1207 -ATL3 1208 -ATM 1209 -ATMIN 1210 -ATN1 1211 -ATOH1 1212 -ATOH7 1213 -ATOH8 1214 -ATOX1 1215 -ATP10A 1216 -ATP10B 1217 -ATP10D 1218 -ATP11A 1219 -ATP11B 1220 -ATP11C 1221 -ATP12A 1222 -ATP13A1 1223 -ATP13A2 1224 -ATP13A3 1225 -ATP13A4 1226 -ATP13A5 1227 -ATP1A1 1228 -ATP1A2 1229 -ATP1A3 1230 -ATP1A4 1231 -ATP1B1 1232 -ATP1B2 1233 -ATP1B3 1234 -ATP1B4 1235 -ATP23 1236 -ATP2A1 1237 -ATP2A2 1238 -ATP2A3 1239 -ATP2B1 1240 -ATP2B2 1241 -ATP2B3 1242 -ATP2B4 1243 -ATP2C1 1244 -ATP2C2 1245 -ATP4A 1246 -ATP4B 1247 -ATP5F1A 1248 -ATP5F1B 1249 -ATP5F1C 1250 -ATP5F1D 1251 -ATP5F1E 1252 -ATP5IF1 1253 -ATP5MC1 1254 -ATP5MC2 1255 -ATP5MC3 1256 -ATP5ME 1257 -ATP5MF 1258 -ATP5MG 1259 -ATP5MGL 1260 -ATP5MJ 1261 -ATP5MK 1262 -ATP5PB 1263 -ATP5PD 1264 -ATP5PF 1265 -ATP5PO 1266 -ATP6AP1 1267 -ATP6AP2 1268 -ATP6V0A1 1269 -ATP6V0A2 1270 -ATP6V0A4 1271 -ATP6V0B 1272 -ATP6V0C 1273 -ATP6V0D1 1274 -ATP6V0D2 1275 -ATP6V0E1 1276 -ATP6V0E2 1277 -ATP6V1A 1278 -ATP6V1B1 1279 -ATP6V1B2 1280 -ATP6V1C1 1281 -ATP6V1C2 1282 -ATP6V1D 1283 -ATP6V1E1 1284 -ATP6V1E2 1285 -ATP6V1F 1286 -ATP6V1FNB 1287 -ATP6V1G1 1288 -ATP6V1G2 1289 -ATP6V1G3 1290 -ATP6V1H 1291 -ATP7A 1292 -ATP7B 1293 -ATP8A1 1294 -ATP8A2 1295 -ATP8B1 1296 -ATP8B2 1297 -ATP8B3 1298 -ATP8B4 1299 -ATP9A 1300 -ATP9B 1301 -ATPAF1 1302 -ATPAF2 1303 -ATPSCKMT 1304 -ATR 1305 -ATRAID 1306 -ATRIP 1307 -ATRN 1308 -ATRNL1 1309 -ATRX 1310 -ATXN1 1311 -ATXN10 1312 -ATXN1L 1313 -ATXN2 1314 -ATXN2L 1315 -ATXN3 1316 -ATXN3L 1317 -ATXN7 1318 -ATXN7L1 1319 -ATXN7L2 1320 -ATXN7L3 1321 -ATXN7L3B 1322 -ATXN8 1323 -AUH 1324 -AUNIP 1325 -AUP1 1326 -AURKA 1327 -AURKAIP1 1328 -AURKB 1329 -AURKC 1330 -AUTS2 1331 -AVEN 1332 -AVIL 1333 -AVL9 1334 -AVP 1335 -AVPI1 1336 -AVPR1A 1337 -AVPR1B 1338 -AVPR2 1339 -AWAT1 1340 -AWAT2 1341 -AXDND1 1342 -AXIN1 1343 -AXIN2 1344 -AXL 1345 -AZGP1 1346 -AZI2 1347 -AZIN1 1348 -AZIN2 1349 -AZU1 1350 -B2M 1351 -B3GALNT1 1352 -B3GALNT2 1353 -B3GALT1 1354 -B3GALT2 1355 -B3GALT4 1356 -B3GALT5 1357 -B3GALT6 1358 -B3GALT9 1359 -B3GAT1 1360 -B3GAT2 1361 -B3GAT3 1362 -B3GLCT 1363 -B3GNT2 1364 -B3GNT3 1365 -B3GNT4 1366 -B3GNT5 1367 -B3GNT6 1368 -B3GNT7 1369 -B3GNT8 1370 -B3GNT9 1371 -B3GNTL1 1372 -B4GALNT1 1373 -B4GALNT2 1374 -B4GALNT3 1375 -B4GALNT4 1376 -B4GALT1 1377 -B4GALT2 1378 -B4GALT3 1379 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1921 -C2orf15 1922 -C2orf16 1923 -C2orf42 1924 -C2orf49 1925 -C2orf50 1926 -C2orf66 1927 -C2orf68 1928 -C2orf69 1929 -C2orf72 1930 -C2orf73 1931 -C2orf74 1932 -C2orf76 1933 -C2orf78 1934 -C2orf80 1935 -C2orf81 1936 -C2orf83 1937 -C2orf88 1938 -C2orf92 1939 -C3 1940 -C3AR1 1941 -C3orf14 1942 -C3orf18 1943 -C3orf20 1944 -C3orf22 1945 -C3orf33 1946 -C3orf38 1947 -C3orf49 1948 -C3orf52 1949 -C3orf56 1950 -C3orf62 1951 -C3orf70 1952 -C3orf80 1953 -C3orf84 1954 -C3orf85 1955 -C4A 1956 -C4B 1957 -C4BPA 1958 -C4BPB 1959 -C4orf17 1960 -C4orf19 1961 -C4orf3 1962 -C4orf33 1963 -C4orf36 1964 -C4orf45 1965 -C4orf46 1966 -C4orf47 1967 -C4orf48 1968 -C4orf50 1969 -C4orf51 1970 -C4orf54 1971 -C5 1972 -C5AR1 1973 -C5AR2 1974 -C5orf15 1975 -C5orf22 1976 -C5orf24 1977 -C5orf34 1978 -C5orf38 1979 -C5orf46 1980 -C5orf47 1981 -C5orf49 1982 -C5orf52 1983 -C5orf58 1984 -C5orf60 1985 -C5orf63 1986 -C6 1987 -C6orf118 1988 -C6orf120 1989 -C6orf132 1990 -C6orf136 1991 -C6orf141 1992 -C6orf15 1993 -C6orf163 1994 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2951 -CGB3 2952 -CGB5 2953 -CGB7 2954 -CGB8 2955 -CGGBP1 2956 -CGN 2957 -CGNL1 2958 -CGREF1 2959 -CGRRF1 2960 -CH25H 2961 -CHAC1 2962 -CHAC2 2963 -CHAD 2964 -CHADL 2965 -CHAF1A 2966 -CHAF1B 2967 -CHAMP1 2968 -CHAT 2969 -CHCHD1 2970 -CHCHD10 2971 -CHCHD2 2972 -CHCHD3 2973 -CHCHD4 2974 -CHCHD5 2975 -CHCHD6 2976 -CHCHD7 2977 -CHD1 2978 -CHD1L 2979 -CHD2 2980 -CHD3 2981 -CHD4 2982 -CHD5 2983 -CHD6 2984 -CHD7 2985 -CHD8 2986 -CHD9 2987 -CHDH 2988 -CHEK1 2989 -CHEK2 2990 -CHERP 2991 -CHFR 2992 -CHGA 2993 -CHGB 2994 -CHI3L1 2995 -CHI3L2 2996 -CHIA 2997 -CHIC1 2998 -CHIC2 2999 -CHID1 3000 -CHIT1 3001 -CHKA 3002 -CHKB 3003 -CHL1 3004 -CHM 3005 -CHML 3006 -CHMP1A 3007 -CHMP1B 3008 -CHMP2A 3009 -CHMP2B 3010 -CHMP3 3011 -CHMP4A 3012 -CHMP4B 3013 -CHMP4C 3014 -CHMP5 3015 -CHMP6 3016 -CHMP7 3017 -CHN1 3018 -CHN2 3019 -CHODL 3020 -CHORDC1 3021 -CHP1 3022 -CHP2 3023 -CHPF 3024 -CHPF2 3025 -CHPT1 3026 -CHRAC1 3027 -CHRD 3028 -CHRDL1 3029 -CHRDL2 3030 -CHRFAM7A 3031 -CHRM1 3032 -CHRM2 3033 -CHRM3 3034 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-CRLF2 3605 -CRLF3 3606 -CRLS1 3607 -CRMP1 3608 -CRNKL1 3609 -CRNN 3610 -CROCC 3611 -CROCC2 3612 -CROT 3613 -CRP 3614 -CRPPA 3615 -CRTAC1 3616 -CRTAM 3617 -CRTAP 3618 -CRTC1 3619 -CRTC2 3620 -CRTC3 3621 -CRX 3622 -CRY1 3623 -CRY2 3624 -CRYAA 3625 -CRYAB 3626 -CRYBA1 3627 -CRYBA2 3628 -CRYBA4 3629 -CRYBB1 3630 -CRYBB2 3631 -CRYBB3 3632 -CRYBG1 3633 -CRYBG2 3634 -CRYBG3 3635 -CRYGA 3636 -CRYGB 3637 -CRYGC 3638 -CRYGD 3639 -CRYGN 3640 -CRYGS 3641 -CRYL1 3642 -CRYM 3643 -CRYZ 3644 -CRYZL1 3645 -CS 3646 -CSAD 3647 -CSAG1 3648 -CSAG2 3649 -CSAG3 3650 -CSDC2 3651 -CSDE1 3652 -CSE1L 3653 -CSF1 3654 -CSF1R 3655 -CSF2 3656 -CSF2RA 3657 -CSF2RB 3658 -CSF3 3659 -CSF3R 3660 -CSGALNACT1 3661 -CSGALNACT2 3662 -CSH1 3663 -CSH2 3664 -CSHL1 3665 -CSK 3666 -CSKMT 3667 -CSMD1 3668 -CSMD2 3669 -CSMD3 3670 -CSN1S1 3671 -CSN2 3672 -CSN3 3673 -CSNK1A1 3674 -CSNK1A1L 3675 -CSNK1D 3676 -CSNK1E 3677 -CSNK1G1 3678 -CSNK1G2 3679 -CSNK1G3 3680 -CSNK2A1 3681 -CSNK2A2 3682 -CSNK2A3 3683 -CSNK2B 3684 -CSNKA2IP 3685 -CSPG4 3686 -CSPG5 3687 -CSPP1 3688 -CSRNP1 3689 -CSRNP2 3690 -CSRNP3 3691 -CSRP1 3692 -CSRP2 3693 -CSRP3 3694 -CST1 3695 -CST11 3696 -CST2 3697 -CST3 3698 -CST4 3699 -CST5 3700 -CST6 3701 -CST7 3702 -CST8 3703 -CST9 3704 -CST9L 3705 -CSTA 3706 -CSTB 3707 -CSTF1 3708 -CSTF2 3709 -CSTF2T 3710 -CSTF3 3711 -CSTL1 3712 -CSTPP1 3713 -CT45A1 3714 -CT45A10 3715 -CT45A2 3716 -CT45A3 3717 -CT45A5 3718 -CT45A6 3719 -CT45A7 3720 -CT45A8 3721 -CT45A9 3722 -CT47A1 3723 -CT47A10 3724 -CT47A11 3725 -CT47A12 3726 -CT47A2 3727 -CT47A3 3728 -CT47A4 3729 -CT47A5 3730 -CT47A6 3731 -CT47A7 3732 -CT47A8 3733 -CT47A9 3734 -CT47B1 3735 -CT55 3736 -CT83 3737 -CTAG1A 3738 -CTAG1B 3739 -CTAG2 3740 -CTAGE1 3741 -CTAGE15 3742 -CTAGE4 3743 -CTAGE6 3744 -CTAGE8 3745 -CTAGE9 3746 -CTBP1 3747 -CTBP2 3748 -CTBS 3749 -CTC1 3750 -CTCF 3751 -CTCFL 3752 -CTDNEP1 3753 -CTDP1 3754 -CTDSP1 3755 -CTDSP2 3756 -CTDSPL 3757 -CTDSPL2 3758 -CTF1 3759 -CTH 3760 -CTHRC1 3761 -CTIF 3762 -CTLA4 3763 -CTNNA1 3764 -CTNNA2 3765 -CTNNA3 3766 -CTNNAL1 3767 -CTNNB1 3768 -CTNNBIP1 3769 -CTNNBL1 3770 -CTNND1 3771 -CTNND2 3772 -CTNS 3773 -CTPS1 3774 -CTPS2 3775 -CTR9 3776 -CTRB1 3777 -CTRB2 3778 -CTRC 3779 -CTRL 3780 -CTSA 3781 -CTSB 3782 -CTSC 3783 -CTSD 3784 -CTSE 3785 -CTSF 3786 -CTSG 3787 -CTSH 3788 -CTSK 3789 -CTSL 3790 -CTSO 3791 -CTSS 3792 -CTSV 3793 -CTSW 3794 -CTSZ 3795 -CTTN 3796 -CTTNBP2 3797 -CTTNBP2NL 3798 -CTU1 3799 -CTU2 3800 -CTXN1 3801 -CTXN2 3802 -CTXN3 3803 -CTXND1 3804 -CTXND2 3805 -CUBN 3806 -CUEDC1 3807 -CUEDC2 3808 -CUL1 3809 -CUL2 3810 -CUL3 3811 -CUL4A 3812 -CUL4B 3813 -CUL5 3814 -CUL7 3815 -CUL9 3816 -CUTA 3817 -CUTC 3818 -CUX1 3819 -CUX2 3820 -CUZD1 3821 -CWC15 3822 -CWC22 3823 -CWC25 3824 -CWC27 3825 -CWF19L1 3826 -CWF19L2 3827 -CWH43 3828 -CX3CL1 3829 -CX3CR1 3830 -CXADR 3831 -CXCL1 3832 -CXCL10 3833 -CXCL11 3834 -CXCL12 3835 -CXCL13 3836 -CXCL14 3837 -CXCL16 3838 -CXCL17 3839 -CXCL2 3840 -CXCL3 3841 -CXCL5 3842 -CXCL6 3843 -CXCL8 3844 -CXCL9 3845 -CXCR1 3846 -CXCR2 3847 -CXCR3 3848 -CXCR4 3849 -CXCR5 3850 -CXCR6 3851 -CXXC1 3852 -CXXC4 3853 -CXXC5 3854 -CXorf38 3855 -CXorf49 3856 -CXorf49B 3857 -CXorf51A 3858 -CXorf51B 3859 -CXorf58 3860 -CXorf65 3861 -CXorf66 3862 -CYB561 3863 -CYB561A3 3864 -CYB561D1 3865 -CYB561D2 3866 -CYB5A 3867 -CYB5B 3868 -CYB5D1 3869 -CYB5D2 3870 -CYB5R1 3871 -CYB5R2 3872 -CYB5R3 3873 -CYB5R4 3874 -CYB5RL 3875 -CYBA 3876 -CYBB 3877 -CYBC1 3878 -CYBRD1 3879 -CYC1 3880 -CYCS 3881 -CYFIP1 3882 -CYFIP2 3883 -CYGB 3884 -CYHR1 3885 -CYLC1 3886 -CYLC2 3887 -CYLD 3888 -CYP11A1 3889 -CYP11B1 3890 -CYP11B2 3891 -CYP17A1 3892 -CYP19A1 3893 -CYP1A1 3894 -CYP1A2 3895 -CYP1B1 3896 -CYP20A1 3897 -CYP21A2 3898 -CYP24A1 3899 -CYP26A1 3900 -CYP26B1 3901 -CYP26C1 3902 -CYP27A1 3903 -CYP27B1 3904 -CYP27C1 3905 -CYP2A13 3906 -CYP2A6 3907 -CYP2A7 3908 -CYP2B6 3909 -CYP2C18 3910 -CYP2C19 3911 -CYP2C8 3912 -CYP2C9 3913 -CYP2D6 3914 -CYP2D7 3915 -CYP2F1 3916 -CYP2J2 3917 -CYP2R1 3918 -CYP2S1 3919 -CYP2U1 3920 -CYP2W1 3921 -CYP39A1 3922 -CYP3A4 3923 -CYP3A43 3924 -CYP3A5 3925 -CYP3A7 3926 -CYP46A1 3927 -CYP4A11 3928 -CYP4A22 3929 -CYP4B1 3930 -CYP4F11 3931 -CYP4F12 3932 -CYP4F2 3933 -CYP4F22 3934 -CYP4F3 3935 -CYP4F8 3936 -CYP4V2 3937 -CYP4X1 3938 -CYP4Z1 3939 -CYP51A1 3940 -CYP7A1 3941 -CYP7B1 3942 -CYP8B1 3943 -CYP20 3944 -CYREN 3945 -CYRIA 3946 -CYRIB 3947 -CYS1 3948 -CYSLTR1 3949 -CYSLTR2 3950 -CYSRT1 3951 -CYSTM1 3952 -CYTH1 3953 -CYTH2 3954 -CYTH3 3955 -CYTH4 3956 -CYTIP 3957 -CYTL1 3958 -CYYR1 3959 -CZIB 3960 -D2HGDH 3961 -DAAM1 3962 -DAAM2 3963 -DAB1 3964 -DAB2 3965 -DAB2IP 3966 -DACH1 3967 -DACH2 3968 -DACT1 3969 -DACT2 3970 -DACT3 3971 -DAD1 3972 -DAG1 3973 -DAGLA 3974 -DAGLB 3975 -DALRD3 3976 -DAND5 3977 -DAO 3978 -DAOA 3979 -DAP 3980 -DAP3 3981 -DAPK1 3982 -DAPK2 3983 -DAPK3 3984 -DAPL1 3985 -DAPP1 3986 -DARS1 3987 -DARS2 3988 -DAW1 3989 -DAXX 3990 -DAZ1 3991 -DAZ2 3992 -DAZ3 3993 -DAZ4 3994 -DAZAP1 3995 -DAZAP2 3996 -DAZL 3997 -DBF4 3998 -DBF4B 3999 -DBH 4000 -DBI 4001 -DBN1 4002 -DBNDD1 4003 -DBNDD2 4004 -DBNL 4005 -DBP 4006 -DBR1 4007 -DBT 4008 -DBX1 4009 -DBX2 4010 -DCAF1 4011 -DCAF10 4012 -DCAF11 4013 -DCAF12 4014 -DCAF12L1 4015 -DCAF12L2 4016 -DCAF13 4017 -DCAF15 4018 -DCAF16 4019 -DCAF17 4020 -DCAF4 4021 -DCAF4L1 4022 -DCAF4L2 4023 -DCAF5 4024 -DCAF6 4025 -DCAF7 4026 -DCAF8 4027 -DCAF8L1 4028 -DCAF8L2 4029 -DCAKD 4030 -DCANP1 4031 -DCBLD1 4032 -DCBLD2 4033 -DCC 4034 -DCD 4035 -DCDC1 4036 -DCDC2 4037 -DCDC2B 4038 -DCDC2C 4039 -DCHS1 4040 -DCHS2 4041 -DCK 4042 -DCLK1 4043 -DCLK2 4044 -DCLK3 4045 -DCLRE1A 4046 -DCLRE1B 4047 -DCLRE1C 4048 -DCN 4049 -DCP1A 4050 -DCP1B 4051 -DCP2 4052 -DCPS 4053 -DCST1 4054 -DCST2 4055 -DCSTAMP 4056 -DCT 4057 -DCTD 4058 -DCTN1 4059 -DCTN2 4060 -DCTN3 4061 -DCTN4 4062 -DCTN5 4063 -DCTN6 4064 -DCTPP1 4065 -DCUN1D1 4066 -DCUN1D2 4067 -DCUN1D3 4068 -DCUN1D4 4069 -DCUN1D5 4070 -DCX 4071 -DCXR 4072 -DDA1 4073 -DDAH1 4074 -DDAH2 4075 -DDB1 4076 -DDB2 4077 -DDC 4078 -DDHD1 4079 -DDHD2 4080 -DDI1 4081 -DDI2 4082 -DDIAS 4083 -DDIT3 4084 -DDIT4 4085 -DDIT4L 4086 -DDN 4087 -DDO 4088 -DDOST 4089 -DDR1 4090 -DDR2 4091 -DDRGK1 4092 -DDT 4093 -DDTL 4094 -DDX1 4095 -DDX10 4096 -DDX11 4097 -DDX17 4098 -DDX18 4099 -DDX19A 4100 -DDX19B 4101 -DDX20 4102 -DDX21 4103 -DDX23 4104 -DDX24 4105 -DDX25 4106 -DDX27 4107 -DDX28 4108 -DDX31 4109 -DDX39A 4110 -DDX39B 4111 -DDX3X 4112 -DDX3Y 4113 -DDX4 4114 -DDX41 4115 -DDX42 4116 -DDX43 4117 -DDX46 4118 -DDX47 4119 -DDX49 4120 -DDX5 4121 -DDX50 4122 -DDX51 4123 -DDX52 4124 -DDX53 4125 -DDX54 4126 -DDX55 4127 -DDX56 4128 -DDX58 4129 -DDX59 4130 -DDX6 4131 -DDX60 4132 -DDX60L 4133 -DEAF1 4134 -DECR1 4135 -DECR2 4136 -DEDD 4137 -DEDD2 4138 -DEF6 4139 -DEF8 4140 -DEFA1 4141 -DEFA1B 4142 -DEFA3 4143 -DEFA4 4144 -DEFA5 4145 -DEFA6 4146 -DEFB1 4147 -DEFB103A 4148 -DEFB103B 4149 -DEFB104A 4150 -DEFB104B 4151 -DEFB105A 4152 -DEFB105B 4153 -DEFB106A 4154 -DEFB106B 4155 -DEFB107A 4156 -DEFB107B 4157 -DEFB108B 4158 -DEFB110 4159 -DEFB112 4160 -DEFB113 4161 -DEFB114 4162 -DEFB115 4163 -DEFB116 4164 -DEFB118 4165 -DEFB119 4166 -DEFB121 4167 -DEFB123 4168 -DEFB124 4169 -DEFB125 4170 -DEFB126 4171 -DEFB127 4172 -DEFB128 4173 -DEFB129 4174 -DEFB130A 4175 -DEFB130B 4176 -DEFB131A 4177 -DEFB131B 4178 -DEFB132 4179 -DEFB134 4180 -DEFB135 4181 -DEFB136 4182 -DEFB4A 4183 -DEFB4B 4184 -DEGS1 4185 -DEGS2 4186 -DEK 4187 -DELE1 4188 -DENND10 4189 -DENND11 4190 -DENND1A 4191 -DENND1B 4192 -DENND1C 4193 -DENND2A 4194 -DENND2B 4195 -DENND2C 4196 -DENND2D 4197 -DENND3 4198 -DENND4A 4199 -DENND4B 4200 -DENND4C 4201 -DENND5A 4202 -DENND5B 4203 -DENND6A 4204 -DENND6B 4205 -DENR 4206 -DEPDC1 4207 -DEPDC1B 4208 -DEPDC4 4209 -DEPDC5 4210 -DEPDC7 4211 -DEPP1 4212 -DEPTOR 4213 -DERA 4214 -DERL1 4215 -DERL2 4216 -DERL3 4217 -DERPC 4218 -DES 4219 -DESI1 4220 -DESI2 4221 -DET1 4222 -DEUP1 4223 -DEXI 4224 -DFFA 4225 -DFFB 4226 -DGAT1 4227 -DGAT2 4228 -DGAT2L6 4229 -DGCR2 4230 -DGCR6 4231 -DGCR6L 4232 -DGCR8 4233 -DGKA 4234 -DGKB 4235 -DGKD 4236 -DGKE 4237 -DGKG 4238 -DGKH 4239 -DGKI 4240 -DGKK 4241 -DGKQ 4242 -DGKZ 4243 -DGLUCY 4244 -DGUOK 4245 -DHCR24 4246 -DHCR7 4247 -DHDDS 4248 -DHDH 4249 -DHFR 4250 -DHFR2 4251 -DHH 4252 -DHODH 4253 -DHPS 4254 -DHRS1 4255 -DHRS11 4256 -DHRS12 4257 -DHRS13 4258 -DHRS2 4259 -DHRS3 4260 -DHRS4 4261 -DHRS4L2 4262 -DHRS7 4263 -DHRS7B 4264 -DHRS7C 4265 -DHRS9 4266 -DHRSX 4267 -DHTKD1 4268 -DHX15 4269 -DHX16 4270 -DHX29 4271 -DHX30 4272 -DHX32 4273 -DHX33 4274 -DHX34 4275 -DHX35 4276 -DHX36 4277 -DHX37 4278 -DHX38 4279 -DHX40 4280 -DHX57 4281 -DHX58 4282 -DHX8 4283 -DHX9 4284 -DIABLO 4285 -DIAPH1 4286 -DIAPH2 4287 -DIAPH3 4288 -DICER1 4289 -DIDO1 4290 -DIMT1 4291 -DIO1 4292 -DIO2 4293 -DIO3 4294 -DIP2A 4295 -DIP2B 4296 -DIP2C 4297 -DIPK1A 4298 -DIPK1B 4299 -DIPK1C 4300 -DIPK2A 4301 -DIPK2B 4302 -DIRAS1 4303 -DIRAS2 4304 -DIRAS3 4305 -DIS3 4306 -DIS3L 4307 -DIS3L2 4308 -DISC1 4309 -DISP1 4310 -DISP2 4311 -DISP3 4312 -DIXDC1 4313 -DKC1 4314 -DKKL1 4315 -DKK1 4316 -DKK2 4317 -DKK3 4318 -DKK4 4319 -DLAT 4320 -DLC1 4321 -DLD 4322 -DLEC1 4323 -DLEU7 4324 -DLG1 4325 -DLG2 4326 -DLG3 4327 -DLG4 4328 -DLG5 4329 -DLGAP1 4330 -DLGAP2 4331 -DLGAP3 4332 -DLGAP4 4333 -DLGAP5 4334 -DLK1 4335 -DLK2 4336 -DLL1 4337 -DLL3 4338 -DLL4 4339 -DLST 4340 -DLX1 4341 -DLX2 4342 -DLX3 4343 -DLX4 4344 -DLX5 4345 -DLX6 4346 -DMAC1 4347 -DMAC2 4348 -DMAC2L 4349 -DMAP1 4350 -DMBT1 4351 -DMBX1 4352 -DMC1 4353 -DMD 4354 -DMGDH 4355 -DMKN 4356 -DMP1 4357 -DMPK 4358 -DMRT1 4359 -DMRT2 4360 -DMRT3 4361 -DMRTA1 4362 -DMRTA2 4363 -DMRTB1 4364 -DMRTC1 4365 -DMRTC1B 4366 -DMRTC2 4367 -DMTF1 4368 -DMTN 4369 -DMWD 4370 -DMXL1 4371 -DMXL2 4372 -DNA2 4373 -DNAAF1 4374 -DNAAF10 4375 -DNAAF11 4376 -DNAAF2 4377 -DNAAF3 4378 -DNAAF4 4379 -DNAAF5 4380 -DNAAF6 4381 -DNAAF8 4382 -DNAAF9 4383 -DNAH1 4384 -DNAH10 4385 -DNAH10OS 4386 -DNAH11 4387 -DNAH12 4388 -DNAH14 4389 -DNAH17 4390 -DNAH2 4391 -DNAH3 4392 -DNAH5 4393 -DNAH6 4394 -DNAH7 4395 -DNAH8 4396 -DNAH9 4397 -DNAI1 4398 -DNAI2 4399 -DNAI3 4400 -DNAI4 4401 -DNAI7 4402 -DNAJA1 4403 -DNAJA2 4404 -DNAJA3 4405 -DNAJA4 4406 -DNAJB1 4407 -DNAJB11 4408 -DNAJB12 4409 -DNAJB13 4410 -DNAJB14 4411 -DNAJB2 4412 -DNAJB4 4413 -DNAJB5 4414 -DNAJB6 4415 -DNAJB7 4416 -DNAJB8 4417 -DNAJB9 4418 -DNAJC1 4419 -DNAJC10 4420 -DNAJC11 4421 -DNAJC12 4422 -DNAJC13 4423 -DNAJC14 4424 -DNAJC15 4425 -DNAJC16 4426 -DNAJC17 4427 -DNAJC18 4428 -DNAJC19 4429 -DNAJC2 4430 -DNAJC21 4431 -DNAJC22 4432 -DNAJC24 4433 -DNAJC25 4434 -DNAJC27 4435 -DNAJC28 4436 -DNAJC3 4437 -DNAJC30 4438 -DNAJC4 4439 -DNAJC5 4440 -DNAJC5B 4441 -DNAJC5G 4442 -DNAJC6 4443 -DNAJC7 4444 -DNAJC8 4445 -DNAJC9 4446 -DNAL1 4447 -DNAL4 4448 -DNALI1 4449 -DNASE1 4450 -DNASE1L1 4451 -DNASE1L2 4452 -DNASE1L3 4453 -DNASE2 4454 -DNASE2B 4455 -DND1 4456 -DNER 4457 -DNHD1 4458 -DNLZ 4459 -DNM1 4460 -DNM1L 4461 -DNM2 4462 -DNM3 4463 -DNMBP 4464 -DNMT1 4465 -DNMT3A 4466 -DNMT3B 4467 -DNMT3L 4468 -DNPEP 4469 -DNPH1 4470 -DNTT 4471 -DNTTIP1 4472 -DNTTIP2 4473 -DOC2A 4474 -DOC2B 4475 -DOCK1 4476 -DOCK10 4477 -DOCK11 4478 -DOCK2 4479 -DOCK3 4480 -DOCK4 4481 -DOCK5 4482 -DOCK6 4483 -DOCK7 4484 -DOCK8 4485 -DOCK9 4486 -DOHH 4487 -DOK1 4488 -DOK2 4489 -DOK3 4490 -DOK4 4491 -DOK5 4492 -DOK6 4493 -DOK7 4494 -DOLK 4495 -DOLPP1 4496 -DONSON 4497 -DOP1A 4498 -DOP1B 4499 -DOT1L 4500 -DPAGT1 4501 -DPCD 4502 -DPEP1 4503 -DPEP2 4504 -DPEP2NB 4505 -DPEP3 4506 -DPF1 4507 -DPF2 4508 -DPF3 4509 -DPH1 4510 -DPH2 4511 -DPH3 4512 -DPH5 4513 -DPH6 4514 -DPH7 4515 -DPM1 4516 -DPM2 4517 -DPM3 4518 -DPP10 4519 -DPP3 4520 -DPP4 4521 -DPP6 4522 -DPP7 4523 -DPP8 4524 -DPP9 4525 -DPPA2 4526 -DPPA3 4527 -DPPA4 4528 -DPPA5 4529 -DPRX 4530 -DPT 4531 -DPY19L1 4532 -DPY19L2 4533 -DPY19L3 4534 -DPY19L4 4535 -DPY30 4536 -DPYD 4537 -DPYS 4538 -DPYSL2 4539 -DPYSL3 4540 -DPYSL4 4541 -DPYSL5 4542 -DQX1 4543 -DR1 4544 -DRAM1 4545 -DRAM2 4546 -DRAP1 4547 -DRAXIN 4548 -DRC1 4549 -DRC3 4550 -DRC7 4551 -DRD1 4552 -DRD2 4553 -DRD3 4554 -DRD4 4555 -DRD5 4556 -DRG1 4557 -DRG2 4558 -DRGX 4559 -DRICH1 4560 -DROSHA 4561 -DRP2 4562 -DSC1 4563 -DSC2 4564 -DSC3 4565 -DSCAM 4566 -DSCAML1 4567 -DSCC1 4568 -DSE 4569 -DSEL 4570 -DSG1 4571 -DSG2 4572 -DSG3 4573 -DSG4 4574 -DSN1 4575 -DSP 4576 -DSPP 4577 -DST 4578 -DSTN 4579 -DSTYK 4580 -DTD1 4581 -DTD2 4582 -DTHD1 4583 -DTL 4584 -DTNA 4585 -DTNB 4586 -DTNBP1 4587 -DTWD1 4588 -DTWD2 4589 -DTX1 4590 -DTX2 4591 -DTX3 4592 -DTX3L 4593 -DTX4 4594 -DTYMK 4595 -DUOX1 4596 -DUOX2 4597 -DUOXA1 4598 -DUOXA2 4599 -DUS1L 4600 -DUS2 4601 -DUS3L 4602 -DUS4L 4603 -DUSP1 4604 -DUSP10 4605 -DUSP11 4606 -DUSP12 4607 -DUSP13 4608 -DUSP14 4609 -DUSP15 4610 -DUSP16 4611 -DUSP18 4612 -DUSP19 4613 -DUSP2 4614 -DUSP21 4615 -DUSP22 4616 -DUSP23 4617 -DUSP26 4618 -DUSP28 4619 -DUSP29 4620 -DUSP3 4621 -DUSP4 4622 -DUSP5 4623 -DUSP6 4624 -DUSP7 4625 -DUSP8 4626 -DUSP9 4627 -DUT 4628 -DUX1 4629 -DUX3 4630 -DUX4 4631 -DUX5 4632 -DUXA 4633 -DUXB 4634 -DVL1 4635 -DVL2 4636 -DVL3 4637 -DXO 4638 -DYDC1 4639 -DYDC2 4640 -DYM 4641 -DYNAP 4642 -DYNC1H1 4643 -DYNC1I1 4644 -DYNC1I2 4645 -DYNC1LI1 4646 -DYNC1LI2 4647 -DYNC2H1 4648 -DYNC2I1 4649 -DYNC2I2 4650 -DYNC2LI1 4651 -DYNLL1 4652 -DYNLL2 4653 -DYNLRB1 4654 -DYNLRB2 4655 -DYNLT1 4656 -DYNLT2 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-KCNJ4 8024 -KCNJ5 8025 -KCNJ6 8026 -KCNJ8 8027 -KCNJ9 8028 -KCNK1 8029 -KCNK10 8030 -KCNK12 8031 -KCNK13 8032 -KCNK15 8033 -KCNK16 8034 -KCNK17 8035 -KCNK18 8036 -KCNK2 8037 -KCNK3 8038 -KCNK4 8039 -KCNK5 8040 -KCNK6 8041 -KCNK7 8042 -KCNK9 8043 -KCNMA1 8044 -KCNMB1 8045 -KCNMB2 8046 -KCNMB3 8047 -KCNMB4 8048 -KCNN1 8049 -KCNN2 8050 -KCNN3 8051 -KCNN4 8052 -KCNQ1 8053 -KCNQ2 8054 -KCNQ3 8055 -KCNQ4 8056 -KCNQ5 8057 -KCNRG 8058 -KCNS1 8059 -KCNS2 8060 -KCNS3 8061 -KCNT1 8062 -KCNT2 8063 -KCNU1 8064 -KCNV1 8065 -KCNV2 8066 -KCP 8067 -KCTD1 8068 -KCTD10 8069 -KCTD11 8070 -KCTD12 8071 -KCTD13 8072 -KCTD14 8073 -KCTD15 8074 -KCTD16 8075 -KCTD17 8076 -KCTD18 8077 -KCTD19 8078 -KCTD2 8079 -KCTD20 8080 -KCTD21 8081 -KCTD3 8082 -KCTD4 8083 -KCTD5 8084 -KCTD6 8085 -KCTD7 8086 -KCTD8 8087 -KCTD9 8088 -KDELR1 8089 -KDELR2 8090 -KDELR3 8091 -KDF1 8092 -KDM1A 8093 -KDM1B 8094 -KDM2A 8095 -KDM2B 8096 -KDM3A 8097 -KDM3B 8098 -KDM4A 8099 -KDM4B 8100 -KDM4C 8101 -KDM4D 8102 -KDM4E 8103 -KDM5A 8104 -KDM5B 8105 -KDM5C 8106 -KDM5D 8107 -KDM6A 8108 -KDM6B 8109 -KDM7A 8110 -KDM8 8111 -KDR 8112 -KDSR 8113 -KEAP1 8114 -KEL 8115 -KERA 8116 -KHDC1 8117 -KHDC1L 8118 -KHDC3L 8119 -KHDC4 8120 -KHDRBS1 8121 -KHDRBS2 8122 -KHDRBS3 8123 -KHK 8124 -KHNYN 8125 -KHSRP 8126 -KIAA0040 8127 -KIAA0100 8128 -KIAA0232 8129 -KIAA0319 8130 -KIAA0319L 8131 -KIAA0408 8132 -KIAA0513 8133 -KIAA0586 8134 -KIAA0753 8135 -KIAA0825 8136 -KIAA0895 8137 -KIAA0895L 8138 -KIAA0930 8139 -KIAA1107 8140 -KIAA1109 8141 -KIAA1143 8142 -KIAA1191 8143 -KIAA1210 8144 -KIAA1217 8145 -KIAA1328 8146 -KIAA1522 8147 -KIAA1549 8148 -KIAA1549L 8149 -KIAA1586 8150 -KIAA1614 8151 -KIAA1671 8152 -KIAA1755 8153 -KIAA1958 8154 -KIAA2012 8155 -KIAA2013 8156 -KIAA2026 8157 -KICS2 8158 -KIDINS220 8159 -KIF11 8160 -KIF12 8161 -KIF13A 8162 -KIF13B 8163 -KIF14 8164 -KIF15 8165 -KIF16B 8166 -KIF17 8167 -KIF18A 8168 -KIF18B 8169 -KIF19 8170 -KIF1A 8171 -KIF1B 8172 -KIF1C 8173 -KIF20A 8174 -KIF20B 8175 -KIF21A 8176 -KIF21B 8177 -KIF22 8178 -KIF23 8179 -KIF24 8180 -KIF25 8181 -KIF26A 8182 -KIF26B 8183 -KIF27 8184 -KIF2A 8185 -KIF2B 8186 -KIF2C 8187 -KIF3A 8188 -KIF3B 8189 -KIF3C 8190 -KIF4A 8191 -KIF4B 8192 -KIF5A 8193 -KIF5B 8194 -KIF5C 8195 -KIF6 8196 -KIF7 8197 -KIF9 8198 -KIFAP3 8199 -KIFBP 8200 -KIFC1 8201 -KIFC2 8202 -KIFC3 8203 -KIN 8204 -KIR2DL1 8205 -KIR2DL3 8206 -KIR2DL4 8207 -KIR2DS4 8208 -KIR3DL1 8209 -KIR3DL2 8210 -KIR3DL3 8211 -KIRREL1 8212 -KIRREL2 8213 -KIRREL3 8214 -KISS1 8215 -KISS1R 8216 -KIT 8217 -KITLG 8218 -KIZ 8219 -KL 8220 -KLB 8221 -KLC1 8222 -KLC2 8223 -KLC3 8224 -KLC4 8225 -KLF1 8226 -KLF10 8227 -KLF11 8228 -KLF12 8229 -KLF13 8230 -KLF14 8231 -KLF15 8232 -KLF16 8233 -KLF17 8234 -KLF18 8235 -KLF2 8236 -KLF3 8237 -KLF4 8238 -KLF5 8239 -KLF6 8240 -KLF7 8241 -KLF8 8242 -KLF9 8243 -KLHDC1 8244 -KLHDC10 8245 -KLHDC2 8246 -KLHDC3 8247 -KLHDC4 8248 -KLHDC7A 8249 -KLHDC7B 8250 -KLHDC8A 8251 -KLHDC8B 8252 -KLHDC9 8253 -KLHL1 8254 -KLHL10 8255 -KLHL11 8256 -KLHL12 8257 -KLHL13 8258 -KLHL14 8259 -KLHL15 8260 -KLHL17 8261 -KLHL18 8262 -KLHL2 8263 -KLHL20 8264 -KLHL21 8265 -KLHL22 8266 -KLHL23 8267 -KLHL24 8268 -KLHL25 8269 -KLHL26 8270 -KLHL28 8271 -KLHL29 8272 -KLHL3 8273 -KLHL30 8274 -KLHL31 8275 -KLHL32 8276 -KLHL33 8277 -KLHL34 8278 -KLHL35 8279 -KLHL36 8280 -KLHL38 8281 -KLHL4 8282 -KLHL40 8283 -KLHL41 8284 -KLHL42 8285 -KLHL5 8286 -KLHL6 8287 -KLHL7 8288 -KLHL8 8289 -KLHL9 8290 -KLK1 8291 -KLK10 8292 -KLK11 8293 -KLK12 8294 -KLK13 8295 -KLK14 8296 -KLK15 8297 -KLK2 8298 -KLK3 8299 -KLK4 8300 -KLK5 8301 -KLK6 8302 -KLK7 8303 -KLK8 8304 -KLK9 8305 -KLKB1 8306 -KLLN 8307 -KLRB1 8308 -KLRC1 8309 -KLRC2 8310 -KLRC3 8311 -KLRC4 8312 -KLRD1 8313 -KLRF1 8314 -KLRF2 8315 -KLRG1 8316 -KLRG2 8317 -KLRK1 8318 -KMO 8319 -KMT2A 8320 -KMT2B 8321 -KMT2C 8322 -KMT2D 8323 -KMT2E 8324 -KMT5A 8325 -KMT5B 8326 -KMT5C 8327 -KNCN 8328 -KNDC1 8329 -KNG1 8330 -KNL1 8331 -KNOP1 8332 -KNSTRN 8333 -KNTC1 8334 -KPNA1 8335 -KPNA2 8336 -KPNA3 8337 -KPNA4 8338 -KPNA5 8339 -KPNA6 8340 -KPNA7 8341 -KPNB1 8342 -KPRP 8343 -KPTN 8344 -KRAS 8345 -KRBA1 8346 -KRBA2 8347 -KRBOX1 8348 -KRBOX4 8349 -KRBOX5 8350 -KRCC1 8351 -KREMEN1 8352 -KREMEN2 8353 -KRI1 8354 -KRIT1 8355 -KRR1 8356 -KRT1 8357 -KRT10 8358 -KRT12 8359 -KRT13 8360 -KRT14 8361 -KRT15 8362 -KRT16 8363 -KRT17 8364 -KRT18 8365 -KRT19 8366 -KRT2 8367 -KRT20 8368 -KRT222 8369 -KRT23 8370 -KRT24 8371 -KRT25 8372 -KRT26 8373 -KRT27 8374 -KRT28 8375 -KRT3 8376 -KRT31 8377 -KRT32 8378 -KRT33A 8379 -KRT33B 8380 -KRT34 8381 -KRT35 8382 -KRT36 8383 -KRT37 8384 -KRT38 8385 -KRT39 8386 -KRT4 8387 -KRT40 8388 -KRT5 8389 -KRT6A 8390 -KRT6B 8391 -KRT6C 8392 -KRT7 8393 -KRT71 8394 -KRT72 8395 -KRT73 8396 -KRT74 8397 -KRT75 8398 -KRT76 8399 -KRT77 8400 -KRT78 8401 -KRT79 8402 -KRT8 8403 -KRT80 8404 -KRT81 8405 -KRT82 8406 -KRT83 8407 -KRT84 8408 -KRT85 8409 -KRT86 8410 -KRT9 8411 -KRTAP1-1 8412 -KRTAP1-3 8413 -KRTAP1-4 8414 -KRTAP1-5 8415 -KRTAP10-1 8416 -KRTAP10-10 8417 -KRTAP10-11 8418 -KRTAP10-12 8419 -KRTAP10-2 8420 -KRTAP10-3 8421 -KRTAP10-4 8422 -KRTAP10-5 8423 -KRTAP10-6 8424 -KRTAP10-7 8425 -KRTAP10-8 8426 -KRTAP10-9 8427 -KRTAP11-1 8428 -KRTAP12-1 8429 -KRTAP12-2 8430 -KRTAP12-3 8431 -KRTAP12-4 8432 -KRTAP13-1 8433 -KRTAP13-2 8434 -KRTAP13-3 8435 -KRTAP13-4 8436 -KRTAP15-1 8437 -KRTAP16-1 8438 -KRTAP17-1 8439 -KRTAP19-1 8440 -KRTAP19-2 8441 -KRTAP19-3 8442 -KRTAP19-4 8443 -KRTAP19-5 8444 -KRTAP19-6 8445 -KRTAP19-7 8446 -KRTAP19-8 8447 -KRTAP2-1 8448 -KRTAP2-2 8449 -KRTAP2-3 8450 -KRTAP2-4 8451 -KRTAP20-1 8452 -KRTAP20-2 8453 -KRTAP20-3 8454 -KRTAP20-4 8455 -KRTAP21-1 8456 -KRTAP21-2 8457 -KRTAP21-3 8458 -KRTAP22-1 8459 -KRTAP22-2 8460 -KRTAP23-1 8461 -KRTAP24-1 8462 -KRTAP25-1 8463 -KRTAP26-1 8464 -KRTAP27-1 8465 -KRTAP29-1 8466 -KRTAP3-1 8467 -KRTAP3-2 8468 -KRTAP3-3 8469 -KRTAP4-1 8470 -KRTAP4-11 8471 -KRTAP4-12 8472 -KRTAP4-16 8473 -KRTAP4-2 8474 -KRTAP4-3 8475 -KRTAP4-4 8476 -KRTAP4-5 8477 -KRTAP4-6 8478 -KRTAP4-7 8479 -KRTAP4-8 8480 -KRTAP4-9 8481 -KRTAP5-1 8482 -KRTAP5-10 8483 -KRTAP5-11 8484 -KRTAP5-2 8485 -KRTAP5-3 8486 -KRTAP5-4 8487 -KRTAP5-5 8488 -KRTAP5-6 8489 -KRTAP5-7 8490 -KRTAP5-8 8491 -KRTAP5-9 8492 -KRTAP6-1 8493 -KRTAP6-2 8494 -KRTAP6-3 8495 -KRTAP7-1 8496 -KRTAP8-1 8497 -KRTAP9-1 8498 -KRTAP9-2 8499 -KRTAP9-3 8500 -KRTAP9-4 8501 -KRTAP9-6 8502 -KRTAP9-7 8503 -KRTAP9-8 8504 -KRTAP9-9 8505 -KRTCAP2 8506 -KRTCAP3 8507 -KRTDAP 8508 -KSR1 8509 -KSR2 8510 -KTI12 8511 -KTN1 8512 -KXD1 8513 -KY 8514 -KYAT1 8515 -KYAT3 8516 -KYNU 8517 -L1CAM 8518 -L1TD1 8519 -L2HGDH 8520 -L3HYPDH 8521 -L3MBTL1 8522 -L3MBTL2 8523 -L3MBTL3 8524 -L3MBTL4 8525 -LACC1 8526 -LACRT 8527 -LACTB 8528 -LACTB2 8529 -LACTBL1 8530 -LAD1 8531 -LAG3 8532 -LAGE3 8533 -LAIR1 8534 -LAIR2 8535 -LALBA 8536 -LAMA1 8537 -LAMA2 8538 -LAMA3 8539 -LAMA4 8540 -LAMA5 8541 -LAMB1 8542 -LAMB2 8543 -LAMB3 8544 -LAMB4 8545 -LAMC1 8546 -LAMC2 8547 -LAMC3 8548 -LAMP1 8549 -LAMP2 8550 -LAMP3 8551 -LAMP5 8552 -LAMTOR1 8553 -LAMTOR2 8554 -LAMTOR3 8555 -LAMTOR4 8556 -LAMTOR5 8557 -LANCL1 8558 -LANCL2 8559 -LANCL3 8560 -LAP3 8561 -LAPTM4A 8562 -LAPTM4B 8563 -LAPTM5 8564 -LARGE1 8565 -LARGE2 8566 -LARP1 8567 -LARP1B 8568 -LARP4 8569 -LARP4B 8570 -LARP6 8571 -LARP7 8572 -LARS1 8573 -LARS2 8574 -LAS1L 8575 -LASP1 8576 -LAT 8577 -LAT2 8578 -LATS1 8579 -LATS2 8580 -LAX1 8581 -LAYN 8582 -LBH 8583 -LBHD1 8584 -LBHD2 8585 -LBP 8586 -LBR 8587 -LBX1 8588 -LBX2 8589 -LCA5 8590 -LCA5L 8591 -LCAT 8592 -LCE1A 8593 -LCE1B 8594 -LCE1C 8595 -LCE1D 8596 -LCE1E 8597 -LCE1F 8598 -LCE2A 8599 -LCE2B 8600 -LCE2C 8601 -LCE2D 8602 -LCE3A 8603 -LCE3B 8604 -LCE3C 8605 -LCE3D 8606 -LCE3E 8607 -LCE4A 8608 -LCE5A 8609 -LCE6A 8610 -LCE7A 8611 -LCK 8612 -LCLAT1 8613 -LCMT1 8614 -LCMT2 8615 -LCN1 8616 -LCN10 8617 -LCN12 8618 -LCN15 8619 -LCN2 8620 -LCN6 8621 -LCN8 8622 -LCN9 8623 -LCNL1 8624 -LCOR 8625 -LCORL 8626 -LCP1 8627 -LCP2 8628 -LCT 8629 -LCTL 8630 -LDAF1 8631 -LDAH 8632 -LDB1 8633 -LDB2 8634 -LDB3 8635 -LDHA 8636 -LDHAL6A 8637 -LDHAL6B 8638 -LDHB 8639 -LDHC 8640 -LDHD 8641 -LDLR 8642 -LDLRAD1 8643 -LDLRAD2 8644 -LDLRAD3 8645 -LDLRAD4 8646 -LDLRAP1 8647 -LDOC1 8648 -LEAP2 8649 -LECT2 8650 -LEF1 8651 -LEFTY1 8652 -LEFTY2 8653 -LEKR1 8654 -LELP1 8655 -LEMD1 8656 -LEMD2 8657 -LEMD3 8658 -LENEP 8659 -LENG1 8660 -LENG8 8661 -LENG9 8662 -LEO1 8663 -LEP 8664 -LEPR 8665 -LEPROT 8666 -LEPROTL1 8667 -LETM1 8668 -LETM2 8669 -LETMD1 8670 -LEUTX 8671 -LEXM 8672 -LFNG 8673 -LGALS1 8674 -LGALS12 8675 -LGALS13 8676 -LGALS14 8677 -LGALS16 8678 -LGALS2 8679 -LGALS3 8680 -LGALS3BP 8681 -LGALS4 8682 -LGALS7 8683 -LGALS7B 8684 -LGALS8 8685 -LGALS9 8686 -LGALS9B 8687 -LGALS9C 8688 -LGALSL 8689 -LGI1 8690 -LGI2 8691 -LGI3 8692 -LGI4 8693 -LGMN 8694 -LGR4 8695 -LGR5 8696 -LGR6 8697 -LGSN 8698 -LHB 8699 -LHCGR 8700 -LHFPL1 8701 -LHFPL2 8702 -LHFPL3 8703 -LHFPL4 8704 -LHFPL5 8705 -LHFPL6 8706 -LHFPL7 8707 -LHPP 8708 -LHX1 8709 -LHX2 8710 -LHX3 8711 -LHX4 8712 -LHX5 8713 -LHX6 8714 -LHX8 8715 -LHX9 8716 -LIAS 8717 -LIF 8718 -LIFR 8719 -LIG1 8720 -LIG3 8721 -LIG4 8722 -LILRA1 8723 -LILRA2 8724 -LILRA4 8725 -LILRA5 8726 -LILRA6 8727 -LILRB1 8728 -LILRB2 8729 -LILRB3 8730 -LILRB4 8731 -LILRB5 8732 -LIM2 8733 -LIMA1 8734 -LIMCH1 8735 -LIMD1 8736 -LIMD2 8737 -LIME1 8738 -LIMK1 8739 -LIMK2 8740 -LIMS1 8741 -LIMS2 8742 -LIMS3 8743 -LIMS4 8744 -LIN28A 8745 -LIN28B 8746 -LIN37 8747 -LIN52 8748 -LIN54 8749 -LIN7A 8750 -LIN7B 8751 -LIN7C 8752 -LIN9 8753 -LINGO1 8754 -LINGO2 8755 -LINGO3 8756 -LINGO4 8757 -LINS1 8758 -LIPA 8759 -LIPC 8760 -LIPE 8761 -LIPF 8762 -LIPG 8763 -LIPH 8764 -LIPI 8765 -LIPJ 8766 -LIPK 8767 -LIPM 8768 -LIPN 8769 -LIPT1 8770 -LIPT2 8771 -LITAF 8772 -LITAFD 8773 -LIX1 8774 -LIX1L 8775 -LKAAEAR1 8776 -LLCFC1 8777 -LLGL1 8778 -LLGL2 8779 -LLPH 8780 -LMAN1 8781 -LMAN1L 8782 -LMAN2 8783 -LMAN2L 8784 -LMBR1 8785 -LMBR1L 8786 -LMBRD1 8787 -LMBRD2 8788 -LMCD1 8789 -LMF1 8790 -LMF2 8791 -LMLN 8792 -LMNA 8793 -LMNB1 8794 -LMNB2 8795 -LMNTD1 8796 -LMNTD2 8797 -LMO1 8798 -LMO2 8799 -LMO3 8800 -LMO4 8801 -LMO7 8802 -LMOD1 8803 -LMOD2 8804 -LMOD3 8805 -LMTK2 8806 -LMTK3 8807 -LMX1A 8808 -LMX1B 8809 -LNP1 8810 -LNPEP 8811 -LNPK 8812 -LNX1 8813 -LNX2 8814 -LONP1 8815 -LONP2 8816 -LONRF1 8817 -LONRF2 8818 -LONRF3 8819 -LORICRIN 8820 -LOX 8821 -LOXHD1 8822 -LOXL1 8823 -LOXL2 8824 -LOXL3 8825 -LOXL4 8826 -LPA 8827 -LPAR1 8828 -LPAR2 8829 -LPAR3 8830 -LPAR4 8831 -LPAR5 8832 -LPAR6 8833 -LPCAT1 8834 -LPCAT2 8835 -LPCAT3 8836 -LPCAT4 8837 -LPGAT1 8838 -LPIN1 8839 -LPIN2 8840 -LPIN3 8841 -LPL 8842 -LPO 8843 -LPP 8844 -LPXN 8845 -LRAT 8846 -LRATD1 8847 -LRATD2 8848 -LRBA 8849 -LRCH1 8850 -LRCH2 8851 -LRCH3 8852 -LRCH4 8853 -LRCOL1 8854 -LRFN1 8855 -LRFN2 8856 -LRFN3 8857 -LRFN4 8858 -LRFN5 8859 -LRG1 8860 -LRGUK 8861 -LRIF1 8862 -LRIG1 8863 -LRIG2 8864 -LRIG3 8865 -LRIT1 8866 -LRIT2 8867 -LRIT3 8868 -LRMDA 8869 -LRP1 8870 -LRP10 8871 -LRP11 8872 -LRP12 8873 -LRP1B 8874 -LRP2 8875 -LRP2BP 8876 -LRP3 8877 -LRP4 8878 -LRP5 8879 -LRP5L 8880 -LRP6 8881 -LRP8 8882 -LRPAP1 8883 -LRPPRC 8884 -LRR1 8885 -LRRC1 8886 -LRRC10 8887 -LRRC10B 8888 -LRRC14 8889 -LRRC14B 8890 -LRRC15 8891 -LRRC17 8892 -LRRC18 8893 -LRRC19 8894 -LRRC2 8895 -LRRC20 8896 -LRRC23 8897 -LRRC24 8898 -LRRC25 8899 -LRRC26 8900 -LRRC27 8901 -LRRC28 8902 -LRRC29 8903 -LRRC3 8904 -LRRC30 8905 -LRRC31 8906 -LRRC32 8907 -LRRC34 8908 -LRRC36 8909 -LRRC37A 8910 -LRRC37A2 8911 -LRRC37A3 8912 -LRRC37B 8913 -LRRC38 8914 -LRRC39 8915 -LRRC3B 8916 -LRRC3C 8917 -LRRC4 8918 -LRRC40 8919 -LRRC41 8920 -LRRC42 8921 -LRRC43 8922 -LRRC45 8923 -LRRC46 8924 -LRRC47 8925 -LRRC49 8926 -LRRC4B 8927 -LRRC4C 8928 -LRRC51 8929 -LRRC52 8930 -LRRC53 8931 -LRRC55 8932 -LRRC56 8933 -LRRC57 8934 -LRRC58 8935 -LRRC59 8936 -LRRC61 8937 -LRRC63 8938 -LRRC66 8939 -LRRC69 8940 -LRRC7 8941 -LRRC70 8942 -LRRC71 8943 -LRRC72 8944 -LRRC73 8945 -LRRC74A 8946 -LRRC74B 8947 -LRRC75A 8948 -LRRC75B 8949 -LRRC8A 8950 -LRRC8B 8951 -LRRC8C 8952 -LRRC8D 8953 -LRRC8E 8954 -LRRC9 8955 -LRRCC1 8956 -LRRD1 8957 -LRRFIP1 8958 -LRRFIP2 8959 -LRRIQ1 8960 -LRRIQ3 8961 -LRRIQ4 8962 -LRRK1 8963 -LRRK2 8964 -LRRN1 8965 -LRRN2 8966 -LRRN3 8967 -LRRN4 8968 -LRRN4CL 8969 -LRRTM1 8970 -LRRTM2 8971 -LRRTM3 8972 -LRRTM4 8973 -LRSAM1 8974 -LRTM1 8975 -LRTM2 8976 -LRWD1 8977 -LSAMP 8978 -LSG1 8979 -LSM14A 8980 -LSM14B 8981 -LSMEM1 8982 -LSMEM2 8983 -LSM1 8984 -LSM10 8985 -LSM11 8986 -LSM12 8987 -LSM2 8988 -LSM3 8989 -LSM4 8990 -LSM5 8991 -LSM6 8992 -LSM7 8993 -LSM8 8994 -LSP1 8995 -LSR 8996 -LSS 8997 -LST1 8998 -LTA 8999 -LTA4H 9000 -LTB 9001 -LTB4R 9002 -LTB4R2 9003 -LTBP1 9004 -LTBP2 9005 -LTBP3 9006 -LTBP4 9007 -LTBR 9008 -LTC4S 9009 -LTF 9010 -LTK 9011 -LTN1 9012 -LTO1 9013 -LTV1 9014 -LUC7L 9015 -LUC7L2 9016 -LUC7L3 9017 -LUM 9018 -LURAP1 9019 -LURAP1L 9020 -LUZP1 9021 -LUZP2 9022 -LUZP4 9023 -LUZP6 9024 -LVRN 9025 -LXN 9026 -LY6D 9027 -LY6E 9028 -LY6G5B 9029 -LY6G5C 9030 -LY6G6C 9031 -LY6G6D 9032 -LY6G6F 9033 -LY6H 9034 -LY6K 9035 -LY6L 9036 -LY75 9037 -LY86 9038 -LY9 9039 -LY96 9040 -LYAR 9041 -LYG1 9042 -LYG2 9043 -LYL1 9044 -LYN 9045 -LYNX1 9046 -LYPD1 9047 -LYPD2 9048 -LYPD3 9049 -LYPD4 9050 -LYPD5 9051 -LYPD6 9052 -LYPD6B 9053 -LYPD8 9054 -LYPLA1 9055 -LYPLA2 9056 -LYPLAL1 9057 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-MAST3 9291 -MAST4 9292 -MASTL 9293 -MAT1A 9294 -MAT2A 9295 -MAT2B 9296 -MATK 9297 -MATN1 9298 -MATN2 9299 -MATN3 9300 -MATN4 9301 -MATR3 9302 -MAU2 9303 -MAVS 9304 -MAX 9305 -MAZ 9306 -MB 9307 -MB21D2 9308 -MBD1 9309 -MBD2 9310 -MBD3 9311 -MBD3L1 9312 -MBD3L2 9313 -MBD3L2B 9314 -MBD3L3 9315 -MBD3L4 9316 -MBD3L5 9317 -MBD4 9318 -MBD5 9319 -MBD6 9320 -MBIP 9321 -MBL2 9322 -MBLAC1 9323 -MBLAC2 9324 -MBNL1 9325 -MBNL2 9326 -MBNL3 9327 -MBOAT1 9328 -MBOAT2 9329 -MBOAT4 9330 -MBOAT7 9331 -MBP 9332 -MBTD1 9333 -MBTPS1 9334 -MBTPS2 9335 -MC1R 9336 -MC2R 9337 -MC3R 9338 -MC4R 9339 -MC5R 9340 -MCAM 9341 -MCAT 9342 -MCC 9343 -MCCC1 9344 -MCCC2 9345 -MCCD1 9346 -MCEE 9347 -MCEMP1 9348 -MCF2L 9349 -MCF2L2 9350 -MCFD2 9351 -MCF2 9352 -MCHR1 9353 -MCHR2 9354 -MCIDAS 9355 -MCL1 9356 -MCM10 9357 -MCM2 9358 -MCM3 9359 -MCM3AP 9360 -MCM4 9361 -MCM5 9362 -MCM6 9363 -MCM7 9364 -MCM8 9365 -MCM9 9366 -MCMBP 9367 -MCMDC2 9368 -MCOLN1 9369 -MCOLN2 9370 -MCOLN3 9371 -MCPH1 9372 -MCRIP1 9373 -MCRIP2 9374 -MCRS1 9375 -MCTP1 9376 -MCTP2 9377 -MCTS1 9378 -MCTS2 9379 -MCU 9380 -MCUB 9381 -MCUR1 9382 -MDC1 9383 -MDFI 9384 -MDFIC 9385 -MDFIC2 9386 -MDGA1 9387 -MDGA2 9388 -MDH1 9389 -MDH1B 9390 -MDH2 9391 -MDK 9392 -MDM1 9393 -MDM2 9394 -MDM4 9395 -MDN1 9396 -MDP1 9397 -ME1 9398 -ME2 9399 -ME3 9400 -MEA1 9401 -MEAF6 9402 -MEAK7 9403 -MECOM 9404 -MECP2 9405 -MECR 9406 -MED1 9407 -MED10 9408 -MED11 9409 -MED12 9410 -MED12L 9411 -MED13 9412 -MED13L 9413 -MED14 9414 -MED14OS 9415 -MED15 9416 -MED16 9417 -MED17 9418 -MED18 9419 -MED19 9420 -MED20 9421 -MED21 9422 -MED22 9423 -MED23 9424 -MED24 9425 -MED25 9426 -MED26 9427 -MED27 9428 -MED28 9429 -MED29 9430 -MED30 9431 -MED31 9432 -MED4 9433 -MED6 9434 -MED7 9435 -MED8 9436 -MED9 9437 -MEDAG 9438 -MEF2A 9439 -MEF2B 9440 -MEF2C 9441 -MEF2D 9442 -MEFV 9443 -MEGF10 9444 -MEGF11 9445 -MEGF6 9446 -MEGF8 9447 -MEGF9 9448 -MEI1 9449 -MEI4 9450 -MEIG1 9451 -MEIKIN 9452 -MEIOB 9453 -MEIOC 9454 -MEIOSIN 9455 -MEIS1 9456 -MEIS2 9457 -MEIS3 9458 -MELK 9459 -MELTF 9460 -MEMO1 9461 -MEN1 9462 -MEOX1 9463 -MEOX2 9464 -MEP1A 9465 -MEP1B 9466 -MEPCE 9467 -MEPE 9468 -MERTK 9469 -MESD 9470 -MESP1 9471 -MESP2 9472 -MEST 9473 -MET 9474 -METAP1 9475 -METAP1D 9476 -METAP2 9477 -METRN 9478 -METRNL 9479 -METTL1 9480 -METTL13 9481 -METTL14 9482 -METTL15 9483 -METTL16 9484 -METTL17 9485 -METTL18 9486 -METTL21A 9487 -METTL21C 9488 -METTL22 9489 -METTL23 9490 -METTL24 9491 -METTL25 9492 -METTL25B 9493 -METTL26 9494 -METTL27 9495 -METTL2A 9496 -METTL2B 9497 -METTL3 9498 -METTL4 9499 -METTL5 9500 -METTL6 9501 -METTL7A 9502 -METTL7B 9503 -METTL8 9504 -METTL9 9505 -MEX3A 9506 -MEX3B 9507 -MEX3C 9508 -MEX3D 9509 -MFAP1 9510 -MFAP2 9511 -MFAP3 9512 -MFAP3L 9513 -MFAP4 9514 -MFAP5 9515 -MFF 9516 -MFGE8 9517 -MFHAS1 9518 -MFN1 9519 -MFN2 9520 -MFNG 9521 -MFRP 9522 -MFSD1 9523 -MFSD10 9524 -MFSD11 9525 -MFSD12 9526 -MFSD13A 9527 -MFSD14A 9528 -MFSD14B 9529 -MFSD14C 9530 -MFSD2A 9531 -MFSD2B 9532 -MFSD3 9533 -MFSD4A 9534 -MFSD4B 9535 -MFSD5 9536 -MFSD6 9537 -MFSD6L 9538 -MFSD8 9539 -MFSD9 9540 -MGA 9541 -MGAM 9542 -MGAM2 9543 -MGARP 9544 -MGAT1 9545 -MGAT2 9546 -MGAT3 9547 -MGAT4A 9548 -MGAT4B 9549 -MGAT4C 9550 -MGAT4D 9551 -MGAT5 9552 -MGAT5B 9553 -MGLL 9554 -MGME1 9555 -MGMT 9556 -MGP 9557 -MGRN1 9558 -MGST1 9559 -MGST2 9560 -MGST3 9561 -MIA 9562 -MIA2 9563 -MIA3 9564 -MIB1 9565 -MIB2 9566 -MICA 9567 -MICAL1 9568 -MICAL2 9569 -MICAL3 9570 -MICALL1 9571 -MICALL2 9572 -MICB 9573 -MICOS10 9574 -MICOS13 9575 -MICU1 9576 -MICU2 9577 -MICU3 9578 -MID1 9579 -MID1IP1 9580 -MID2 9581 -MIDEAS 9582 -MIDN 9583 -MIEF1 9584 -MIEF2 9585 -MIEN1 9586 -MIER1 9587 -MIER2 9588 -MIER3 9589 -MIF 9590 -MIF4GD 9591 -MIGA1 9592 -MIGA2 9593 -MIIP 9594 -MILR1 9595 -MINAR1 9596 -MINAR2 9597 -MINDY1 9598 -MINDY2 9599 -MINDY3 9600 -MINDY4 9601 -MINDY4B 9602 -MINK1 9603 -MINPP1 9604 -MIOS 9605 -MIOX 9606 -MIP 9607 -MIPEP 9608 -MIPOL1 9609 -MIS12 9610 -MIS18A 9611 -MIS18BP1 9612 -MISP 9613 -MISP3 9614 -MITD1 9615 -MITF 9616 -MIX23 9617 -MIXL1 9618 -MKI67 9619 -MKKS 9620 -MKLN1 9621 -MKNK1 9622 -MKNK2 9623 -MKRN1 9624 -MKRN2 9625 -MKRN2OS 9626 -MKRN3 9627 -MKS1 9628 -MKX 9629 -MLANA 9630 -MLC1 9631 -MLEC 9632 -MLF1 9633 -MLF2 9634 -MLH1 9635 -MLH3 9636 -MLIP 9637 -MLKL 9638 -MLLT1 9639 -MLLT10 9640 -MLLT11 9641 -MLLT3 9642 -MLLT6 9643 -MLN 9644 -MLNR 9645 -MLPH 9646 -MLST8 9647 -MLX 9648 -MLXIP 9649 -MLXIPL 9650 -MLYCD 9651 -MMAA 9652 -MMAB 9653 -MMACHC 9654 -MMADHC 9655 -MMD 9656 -MMD2 9657 -MME 9658 -MMEL1 9659 -MMGT1 9660 -MMP1 9661 -MMP10 9662 -MMP11 9663 -MMP12 9664 -MMP13 9665 -MMP14 9666 -MMP15 9667 -MMP16 9668 -MMP17 9669 -MMP19 9670 -MMP2 9671 -MMP20 9672 -MMP21 9673 -MMP23B 9674 -MMP24 9675 -MMP24OS 9676 -MMP25 9677 -MMP26 9678 -MMP27 9679 -MMP28 9680 -MMP3 9681 -MMP7 9682 -MMP8 9683 -MMP9 9684 -MMRN1 9685 -MMRN2 9686 -MMS19 9687 -MMS22L 9688 -MMUT 9689 -MN1 9690 -MNAT1 9691 -MND1 9692 -MNDA 9693 -MNS1 9694 -MNT 9695 -MNX1 9696 -MOAP1 9697 -MOB1A 9698 -MOB1B 9699 -MOB2 9700 -MOB3A 9701 -MOB3B 9702 -MOB3C 9703 -MOB4 9704 -MOBP 9705 -MOCOS 9706 -MOCS1 9707 -MOCS2 9708 -MOCS3 9709 -MOG 9710 -MOGAT1 9711 -MOGAT2 9712 -MOGAT3 9713 -MOGS 9714 -MOK 9715 -MON1A 9716 -MON1B 9717 -MON2 9718 -MORC1 9719 -MORC2 9720 -MORC3 9721 -MORC4 9722 -MORF4L1 9723 -MORF4L2 9724 -MORN1 9725 -MORN2 9726 -MORN3 9727 -MORN4 9728 -MORN5 9729 -MOS 9730 -MOSMO 9731 -MOSPD1 9732 -MOSPD2 9733 -MOSPD3 9734 -MOV10 9735 -MOV10L1 9736 -MOXD1 9737 -MPC1 9738 -MPC1L 9739 -MPC2 9740 -MPDU1 9741 -MPDZ 9742 -MPEG1 9743 -MPG 9744 -MPHOSPH10 9745 -MPHOSPH6 9746 -MPHOSPH8 9747 -MPHOSPH9 9748 -MPI 9749 -MPIG6B 9750 -MPL 9751 -MPLKIP 9752 -MPND 9753 -MPO 9754 -MPP1 9755 -MPP2 9756 -MPP3 9757 -MPP4 9758 -MPP7 9759 -MPPE1 9760 -MPPED1 9761 -MPPED2 9762 -MPRIP 9763 -MPST 9764 -MPV17 9765 -MPV17L 9766 -MPV17L2 9767 -MPZ 9768 -MPZL1 9769 -MPZL2 9770 -MPZL3 9771 -MR1 9772 -MRAP 9773 -MRAP2 9774 -MRAS 9775 -MRC1 9776 -MRC2 9777 -MRE11 9778 -MREG 9779 -MRFAP1 9780 -MRFAP1L1 9781 -MRGBP 9782 -MRGPRD 9783 -MRGPRE 9784 -MRGPRF 9785 -MRGPRG 9786 -MRGPRX1 9787 -MRGPRX2 9788 -MRGPRX3 9789 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9868 -MRPS24 9869 -MRPS25 9870 -MRPS26 9871 -MRPS27 9872 -MRPS28 9873 -MRPS30 9874 -MRPS31 9875 -MRPS33 9876 -MRPS34 9877 -MRPS35 9878 -MRPS36 9879 -MRPS5 9880 -MRPS6 9881 -MRPS7 9882 -MRPS9 9883 -MRRF 9884 -MRS2 9885 -MRTFA 9886 -MRTFB 9887 -MRTO4 9888 -MS4A1 9889 -MS4A10 9890 -MS4A12 9891 -MS4A13 9892 -MS4A14 9893 -MS4A15 9894 -MS4A18 9895 -MS4A2 9896 -MS4A3 9897 -MS4A4A 9898 -MS4A4E 9899 -MS4A5 9900 -MS4A6A 9901 -MS4A6E 9902 -MS4A7 9903 -MS4A8 9904 -MSANTD1 9905 -MSANTD2 9906 -MSANTD3 9907 -MSANTD4 9908 -MSANTD5 9909 -MSC 9910 -MSGN1 9911 -MSH2 9912 -MSH3 9913 -MSH4 9914 -MSH5 9915 -MSH6 9916 -MSI1 9917 -MSI2 9918 -MSL1 9919 -MSL2 9920 -MSL3 9921 -MSLN 9922 -MSMB 9923 -MSMO1 9924 -MSMP 9925 -MSN 9926 -MSR1 9927 -MSRA 9928 -MSRB1 9929 -MSRB2 9930 -MSRB3 9931 -MSS51 9932 -MST1 9933 -MST1R 9934 -MSTN 9935 -MSTO1 9936 -MSX1 9937 -MSX2 9938 -MT-ATP6 9939 -MT-ATP6__chrM 9940 -MT-ATP8 9941 -MT-ATP8__chrM 9942 -MT-CO1 9943 -MT-CO1__chrM 9944 -MT-CO2 9945 -MT-CO2__chrM 9946 -MT-CO3 9947 -MT-CO3__chrM 9948 -MT-CYB 9949 -MT-CYB__chrM 9950 -MT-ND1 9951 -MT-ND1__chrM 9952 -MT-ND2 9953 -MT-ND2__chrM 9954 -MT-ND3 9955 -MT-ND3__chrM 9956 -MT-ND4 9957 -MT-ND4L 9958 -MT-ND4L__chrM 9959 -MT-ND4__chrM 9960 -MT-ND5 9961 -MT-ND5__chrM 9962 -MT-ND6 9963 -MT-ND6__chrM 9964 -MT-RNR1 9965 -MT-RNR1__chrM 9966 -MT-RNR2 9967 -MT-RNR2__chrM 9968 -MT-TA 9969 -MT-TC 9970 -MT-TD 9971 -MT-TE 9972 -MT-TF 9973 -MT-TF__chrM 9974 -MT-TG 9975 -MT-TH 9976 -MT-TH__chrM 9977 -MT-TI 9978 -MT-TK 9979 -MT-TK__chrM 9980 -MT-TL1 9981 -MT-TL2 9982 -MT-TL2__chrM 9983 -MT-TM 9984 -MT-TN 9985 -MT-TP 9986 -MT-TQ 9987 -MT-TQ__chrM 9988 -MT-TR 9989 -MT-TS1 9990 -MT-TS2 9991 -MT-TT 9992 -MT-TV 9993 -MT-TW 9994 -MT-TY 9995 -MT1A 9996 -MT1B 9997 -MT1E 9998 -MT1F 9999 -MT1G 10000 -MT1H 10001 -MT1HL1 10002 -MT1M 10003 -MT1X 10004 -MT2A 10005 -MT3 10006 -MT4 10007 -MTA1 10008 -MTA2 10009 -MTA3 10010 -MTAP 10011 -MTARC1 10012 -MTARC2 10013 -MTBP 10014 -MTCH1 10015 -MTCH2 10016 -MTCL1 10017 -MTCP1 10018 -MTDH 10019 -MTERF1 10020 -MTERF2 10021 -MTERF3 10022 -MTERF4 10023 -MTF1 10024 -MTF2 10025 -MTFMT 10026 -MTFP1 10027 -MTFR1 10028 -MTFR1L 10029 -MTFR2 10030 -MTG1 10031 -MTG2 10032 -MTHFD1 10033 -MTHFD1L 10034 -MTHFD2 10035 -MTHFD2L 10036 -MTHFR 10037 -MTHFS 10038 -MTHFSD 10039 -MTIF2 10040 -MTIF3 10041 -MTLN 10042 -MTM1 10043 -MTMR1 10044 -MTMR10 10045 -MTMR11 10046 -MTMR12 10047 -MTMR14 10048 -MTMR2 10049 -MTMR3 10050 -MTMR4 10051 -MTMR6 10052 -MTMR7 10053 -MTMR8 10054 -MTMR9 10055 -MTNR1A 10056 -MTNR1B 10057 -MTO1 10058 -MTOR 10059 -MTPAP 10060 -MTPN 10061 -MTR 10062 -MTRES1 10063 -MTREX 10064 -MTRF1 10065 -MTRF1L 10066 -MTRFR 10067 -MTRR 10068 -MTSS1 10069 -MTSS2 10070 -MTTP 10071 -MTURN 10072 -MTUS1 10073 -MTUS2 10074 -MTX1 10075 -MTX2 10076 -MTX3 10077 -MUC1 10078 -MUC12 10079 -MUC13 10080 -MUC15 10081 -MUC16 10082 -MUC17 10083 -MUC19 10084 -MUC2 10085 -MUC20 10086 -MUC21 10087 -MUC22 10088 -MUC3A 10089 -MUC3B 10090 -MUC4 10091 -MUC5AC 10092 -MUC5B 10093 -MUC6 10094 -MUC7 10095 -MUC8 10096 -MUCL1 10097 -MUCL3 10098 -MUL1 10099 -MUS81 10100 -MUSK 10101 -MUSTN1 10102 -MUTYH 10103 -MVB12A 10104 -MVB12B 10105 -MVD 10106 -MVK 10107 -MVP 10108 -MX1 10109 -MX2 10110 -MXD1 10111 -MXD3 10112 -MXD4 10113 -MXI1 10114 -MXRA5 10115 -MXRA7 10116 -MXRA8 10117 -MYADM 10118 -MYADML2 10119 -MYB 10120 -MYBBP1A 10121 -MYBL1 10122 -MYBL2 10123 -MYBPC1 10124 -MYBPC2 10125 -MYBPC3 10126 -MYBPH 10127 -MYBPHL 10128 -MYC 10129 -MYCBP 10130 -MYCBP2 10131 -MYCBPAP 10132 -MYCL 10133 -MYCN 10134 -MYCT1 10135 -MYD88 10136 -MYDGF 10137 -MYEF2 10138 -MYEOV 10139 -MYF5 10140 -MYF6 10141 -MYG1 10142 -MYH1 10143 -MYH10 10144 -MYH11 10145 -MYH13 10146 -MYH14 10147 -MYH15 10148 -MYH2 10149 -MYH3 10150 -MYH4 10151 -MYH6 10152 -MYH7 10153 -MYH7B 10154 -MYH8 10155 -MYH9 10156 -MYL1 10157 -MYL10 10158 -MYL12A 10159 -MYL12B 10160 -MYL2 10161 -MYL3 10162 -MYL4 10163 -MYL5 10164 -MYL6 10165 -MYL6B 10166 -MYL7 10167 -MYL9 10168 -MYLIP 10169 -MYLK 10170 -MYLK2 10171 -MYLK3 10172 -MYLK4 10173 -MYLPF 10174 -MYMK 10175 -MYMX 10176 -MYNN 10177 -MYO10 10178 -MYO15A 10179 -MYO15B 10180 -MYO16 10181 -MYO18A 10182 -MYO18B 10183 -MYO19 10184 -MYO1A 10185 -MYO1B 10186 -MYO1C 10187 -MYO1D 10188 -MYO1E 10189 -MYO1F 10190 -MYO1G 10191 -MYO1H 10192 -MYO3A 10193 -MYO3B 10194 -MYO5A 10195 -MYO5B 10196 -MYO5C 10197 -MYO6 10198 -MYO7A 10199 -MYO7B 10200 -MYO9A 10201 -MYO9B 10202 -MYOC 10203 -MYOCD 10204 -MYOCOS 10205 -MYOD1 10206 -MYOF 10207 -MYOG 10208 -MYOM1 10209 -MYOM2 10210 -MYOM3 10211 -MYORG 10212 -MYOT 10213 -MYOZ1 10214 -MYOZ2 10215 -MYOZ3 10216 -MYPN 10217 -MYPOP 10218 -MYRF 10219 -MYRFL 10220 -MYRIP 10221 -MYSM1 10222 -MYT1 10223 -MYT1L 10224 -MYZAP 10225 -MZB1 10226 -MZF1 10227 -MZT1 10228 -MZT2A 10229 -MZT2B 10230 -N4BP1 10231 -N4BP2 10232 -N4BP2L1 10233 -N4BP2L2 10234 -N4BP3 10235 -N6AMT1 10236 -NAA10 10237 -NAA11 10238 -NAA15 10239 -NAA16 10240 -NAA20 10241 -NAA25 10242 -NAA30 10243 -NAA35 10244 -NAA38 10245 -NAA40 10246 -NAA50 10247 -NAA60 10248 -NAA80 10249 -NAAA 10250 -NAALAD2 10251 -NAALADL1 10252 -NAALADL2 10253 -NAB1 10254 -NAB2 10255 -NABP1 10256 -NABP2 10257 -NACA 10258 -NACA2 10259 -NACAD 10260 -NACC1 10261 -NACC2 10262 -NADK 10263 -NADK2 10264 -NADSYN1 10265 -NAE1 10266 -NAF1 10267 -NAGA 10268 -NAGK 10269 -NAGLU 10270 -NAGPA 10271 -NAGS 10272 -NAIF1 10273 -NAIP 10274 -NALCN 10275 -NALF1 10276 -NALF2 10277 -NAMPT 10278 -NANOG 10279 -NANOGNB 10280 -NANOGP8 10281 -NANOS1 10282 -NANOS2 10283 -NANOS3 10284 -NANP 10285 -NANS 10286 -NAP1L1 10287 -NAP1L2 10288 -NAP1L3 10289 -NAP1L4 10290 -NAP1L5 10291 -NAPA 10292 -NAPB 10293 -NAPEPLD 10294 -NAPG 10295 -NAPRT 10296 -NAPSA 10297 -NARF 10298 -NARS1 10299 -NARS2 10300 -NASP 10301 -NAT1 10302 -NAT10 10303 -NAT14 10304 -NAT16 10305 -NAT2 10306 -NAT8 10307 -NAT8B 10308 -NAT8L 10309 -NAT9 10310 -NATD1 10311 -NAV1 10312 -NAV2 10313 -NAV3 10314 -NAXD 10315 -NAXE 10316 -NBArS 10317 -NBDY 10318 -NBEA 10319 -NBEAL1 10320 -NBEAL2 10321 -NBL1 10322 -NBN 10323 -NBPF1 10324 -NBPF10 10325 -NBPF11 10326 -NBPF12 10327 -NBPF14 10328 -NBPF15 10329 -NBPF19 10330 -NBPF20 10331 -NBPF26 10332 -NBPF3 10333 -NBPF4 10334 -NBPF6 10335 -NBPF9 10336 -NBR1 10337 -NCALD 10338 -NCAM1 10339 -NCAM2 10340 -NCAN 10341 -NCAPD2 10342 -NCAPD3 10343 -NCAPG 10344 -NCAPG2 10345 -NCAPH 10346 -NCAPH2 10347 -NCBP1 10348 -NCBP2 10349 -NCBP2AS2 10350 -NCBP2L 10351 -NCBP3 10352 -NCCRP1 10353 -NCDN 10354 -NCEH1 10355 -NCF1 10356 -NCF2 10357 -NCF4 10358 -NCK1 10359 -NCK2 10360 -NCKAP1 10361 -NCKAP1L 10362 -NCKAP5 10363 -NCKAP5L 10364 -NCKIPSD 10365 -NCL 10366 -NCLN 10367 -NCMAP 10368 -NCOA1 10369 -NCOA2 10370 -NCOA3 10371 -NCOA4 10372 -NCOA5 10373 -NCOA6 10374 -NCOA7 10375 -NCOR1 10376 -NCOR2 10377 -NCR1 10378 -NCR2 10379 -NCR3 10380 -NCR3LG1 10381 -NCS1 10382 -NCSTN 10383 -NDC1 10384 -NDC80 10385 -NDE1 10386 -NDEL1 10387 -NDFIP1 10388 -NDFIP2 10389 -NDN 10390 -NDNF 10391 -NDOR1 10392 -NDP 10393 -NDRG1 10394 -NDRG2 10395 -NDRG3 10396 -NDRG4 10397 -NDST1 10398 -NDST2 10399 -NDST3 10400 -NDST4 10401 -NDUFA1 10402 -NDUFA10 10403 -NDUFA11 10404 -NDUFA12 10405 -NDUFA13 10406 -NDUFA2 10407 -NDUFA3 10408 -NDUFA4 10409 -NDUFA4L2 10410 -NDUFA5 10411 -NDUFA6 10412 -NDUFA7 10413 -NDUFA8 10414 -NDUFA9 10415 -NDUFAB1 10416 -NDUFAF1 10417 -NDUFAF2 10418 -NDUFAF3 10419 -NDUFAF4 10420 -NDUFAF5 10421 -NDUFAF6 10422 -NDUFAF7 10423 -NDUFAF8 10424 -NDUFB1 10425 -NDUFB10 10426 -NDUFB11 10427 -NDUFB2 10428 -NDUFB3 10429 -NDUFB4 10430 -NDUFB5 10431 -NDUFB6 10432 -NDUFB7 10433 -NDUFB8 10434 -NDUFB9 10435 -NDUFC1 10436 -NDUFC2 10437 -NDUFS1 10438 -NDUFS2 10439 -NDUFS3 10440 -NDUFS4 10441 -NDUFS5 10442 -NDUFS6 10443 -NDUFS7 10444 -NDUFS8 10445 -NDUFV1 10446 -NDUFV2 10447 -NDUFV3 10448 -NEB 10449 -NEBL 10450 -NECAB1 10451 -NECAB2 10452 -NECAB3 10453 -NECAP1 10454 -NECAP2 10455 -NECTIN1 10456 -NECTIN2 10457 -NECTIN3 10458 -NECTIN4 10459 -NEDD1 10460 -NEDD4 10461 -NEDD4L 10462 -NEDD8 10463 -NEDD9 10464 -NEFH 10465 -NEFL 10466 -NEFM 10467 -NEGR1 10468 -NEIL1 10469 -NEIL2 10470 -NEIL3 10471 -NEK1 10472 -NEK10 10473 -NEK11 10474 -NEK2 10475 -NEK3 10476 -NEK4 10477 -NEK5 10478 -NEK6 10479 -NEK7 10480 -NEK8 10481 -NEK9 10482 -NELFA 10483 -NELFB 10484 -NELFCD 10485 -NELFE 10486 -NELL1 10487 -NELL2 10488 -NEMF 10489 -NEMP1 10490 -NEMP2 10491 -NENF 10492 -NEO1 10493 -NEPRO 10494 -NES 10495 -NET1 10496 -NETO1 10497 -NETO2 10498 -NEU1 10499 -NEU2 10500 -NEU3 10501 -NEU4 10502 -NEURL1 10503 -NEURL1B 10504 -NEURL2 10505 -NEURL3 10506 -NEURL4 10507 -NEUROD1 10508 -NEUROD2 10509 -NEUROD4 10510 -NEUROD6 10511 -NEUROG1 10512 -NEUROG2 10513 -NEUROG3 10514 -NEXMIF 10515 -NEXN 10516 -NF1 10517 -NF2 10518 -NFAM1 10519 -NFASC 10520 -NFAT5 10521 -NFATC1 10522 -NFATC2 10523 -NFATC2IP 10524 -NFATC3 10525 -NFATC4 10526 -NFE2 10527 -NFE2L1 10528 -NFE2L2 10529 -NFE2L3 10530 -NFIA 10531 -NFIB 10532 -NFIC 10533 -NFIL3 10534 -NFILZ 10535 -NFIX 10536 -NFKB1 10537 -NFKB2 10538 -NFKBIA 10539 -NFKBIB 10540 -NFKBID 10541 -NFKBIE 10542 -NFKBIL1 10543 -NFKBIZ 10544 -NFRKB 10545 -NFS1 10546 -NFU1 10547 -NFX1 10548 -NFXL1 10549 -NFYA 10550 -NFYB 10551 -NFYC 10552 -NGB 10553 -NGDN 10554 -NGEF 10555 -NGF 10556 -NGFR 10557 -NGLY1 10558 -NGRN 10559 -NHEJ1 10560 -NHLH1 10561 -NHLH2 10562 -NHLRC1 10563 -NHLRC2 10564 -NHLRC3 10565 -NHLRC4 10566 -NHP2 10567 -NHS 10568 -NHSL1 10569 -NHSL2 10570 -NIBAN1 10571 -NIBAN2 10572 -NIBAN3 10573 -NICN1 10574 -NID1 10575 -NID2 10576 -NIF3L1 10577 -NIFK 10578 -NIM1K 10579 -NIN 10580 -NINJ1 10581 -NINJ2 10582 -NINL 10583 -NIP7 10584 -NIPA1 10585 -NIPA2 10586 -NIPAL1 10587 -NIPAL2 10588 -NIPAL3 10589 -NIPAL4 10590 -NIPBL 10591 -NIPSNAP1 10592 -NIPSNAP2 10593 -NIPSNAP3A 10594 -NIPSNAP3B 10595 -NISCH 10596 -NIT1 10597 -NIT2 10598 -NKAIN1 10599 -NKAIN2 10600 -NKAIN3 10601 -NKAIN4 10602 -NKAP 10603 -NKAPD1 10604 -NKAPL 10605 -NKD1 10606 -NKD2 10607 -NKG7 10608 -NKIRAS1 10609 -NKIRAS2 10610 -NKPD1 10611 -NKRF 10612 -NKTR 10613 -NKX1-1 10614 -NKX1-2 10615 -NKX2-1 10616 -NKX2-2 10617 -NKX2-3 10618 -NKX2-4 10619 -NKX2-5 10620 -NKX2-6 10621 -NKX2-8 10622 -NKX3-1 10623 -NKX3-2 10624 -NKX6-1 10625 -NKX6-2 10626 -NKX6-3 10627 -NLE1 10628 -NLGN1 10629 -NLGN2 10630 -NLGN3 10631 -NLGN4X 10632 -NLGN4Y 10633 -NLK 10634 -NLN 10635 -NLRC3 10636 -NLRC4 10637 -NLRC5 10638 -NLRP1 10639 -NLRP10 10640 -NLRP11 10641 -NLRP12 10642 -NLRP13 10643 -NLRP14 10644 -NLRP2 10645 -NLRP2B 10646 -NLRP3 10647 -NLRP4 10648 -NLRP5 10649 -NLRP6 10650 -NLRP7 10651 -NLRP8 10652 -NLRP9 10653 -NLRX1 10654 -NMB 10655 -NMBR 10656 -NMD3 10657 -NME1 10658 -NME2 10659 -NME3 10660 -NME4 10661 -NME5 10662 -NME6 10663 -NME7 10664 -NME8 10665 -NME9 10666 -NMI 10667 -NMNAT1 10668 -NMNAT2 10669 -NMNAT3 10670 -NMRAL1 10671 -NMRK1 10672 -NMRK2 10673 -NMS 10674 -NMT1 10675 -NMT2 10676 -NMU 10677 -NMUR1 10678 -NMUR2 10679 -NNAT 10680 -NNMT 10681 -NNT 10682 -NOA1 10683 -NOB1 10684 -NOBOX 10685 -NOC2L 10686 -NOC3L 10687 -NOC4L 10688 -NOCT 10689 -NOD1 10690 -NOD2 10691 -NODAL 10692 -NOG 10693 -NOL10 10694 -NOL11 10695 -NOL12 10696 -NOL3 10697 -NOL4 10698 -NOL4L 10699 -NOL6 10700 -NOL7 10701 -NOL8 10702 -NOL9 10703 -NOLC1 10704 -NOM1 10705 -NOMO1 10706 -NOMO2 10707 -NOMO3 10708 -NONO 10709 -NOP10 10710 -NOP14 10711 -NOP16 10712 -NOP2 10713 -NOP53 10714 -NOP56 10715 -NOP58 10716 -NOP9 10717 -NOPCHAP1 10718 -NOS1 10719 -NOS1AP 10720 -NOS2 10721 -NOS3 10722 -NOSIP 10723 -NOSTRIN 10724 -NOTCH1 10725 -NOTCH2 10726 -NOTCH2NLA 10727 -NOTCH2NLB 10728 -NOTCH2NLC 10729 -NOTCH3 10730 -NOTCH4 10731 -NOTO 10732 -NOTUM 10733 -NOVA1 10734 -NOVA2 10735 -NOX1 10736 -NOX3 10737 -NOX4 10738 -NOX5 10739 -NOXA1 10740 -NOXO1 10741 -NOXRED1 10742 -NPAP1 10743 -NPAP1L 10744 -NPAS1 10745 -NPAS2 10746 -NPAS3 10747 -NPAS4 10748 -NPAT 10749 -NPB 10750 -NPBWR1 10751 -NPBWR2 10752 -NPC1 10753 -NPC1L1 10754 -NPC2 10755 -NPDC1 10756 -NPEPL1 10757 -NPEPPS 10758 -NPFF 10759 -NPFFR1 10760 -NPFFR2 10761 -NPHP1 10762 -NPHP3 10763 -NPHP4 10764 -NPHS1 10765 -NPHS2 10766 -NPIPA1 10767 -NPIPA2 10768 -NPIPA3 10769 -NPIPA5 10770 -NPIPA7 10771 -NPIPA8 10772 -NPIPB11 10773 -NPIPB12 10774 -NPIPB13 10775 -NPIPB15 10776 -NPIPB2 10777 -NPIPB3 10778 -NPIPB4 10779 -NPIPB5 10780 -NPIPB6 10781 -NPIPB8 10782 -NPIPB9 10783 -NPL 10784 -NPLOC4 10785 -NPM1 10786 -NPM2 10787 -NPM3 10788 -NPNT 10789 -NPPA 10790 -NPPB 10791 -NPPC 10792 -NPRL2 10793 -NPRL3 10794 -NPR1 10795 -NPR2 10796 -NPR3 10797 -NPS 10798 -NPSR1 10799 -NPTN 10800 -NPTX1 10801 -NPTX2 10802 -NPTXR 10803 -NPVF 10804 -NPW 10805 -NPY 10806 -NPY1R 10807 -NPY2R 10808 -NPY4R 10809 -NPY4R2 10810 -NPY5R 10811 -NQO1 10812 -NQO2 10813 -NR0B1 10814 -NR0B2 10815 -NR1D1 10816 -NR1D2 10817 -NR1H2 10818 -NR1H3 10819 -NR1H4 10820 -NR1I2 10821 -NR1I3 10822 -NR2C1 10823 -NR2C2 10824 -NR2C2AP 10825 -NR2E1 10826 -NR2E3 10827 -NR2F1 10828 -NR2F2 10829 -NR2F6 10830 -NR3C1 10831 -NR3C2 10832 -NR4A1 10833 -NR4A2 10834 -NR4A3 10835 -NR5A1 10836 -NR5A2 10837 -NR6A1 10838 -NRAP 10839 -NRARP 10840 -NRAS 10841 -NRBF2 10842 -NRBP1 10843 -NRBP2 10844 -NRCAM 10845 -NRDC 10846 -NRDE2 10847 -NREP 10848 -NRF1 10849 -NRG1 10850 -NRG2 10851 -NRG3 10852 -NRG4 10853 -NRGN 10854 -NRIP1 10855 -NRIP2 10856 -NRIP3 10857 -NRK 10858 -NRL 10859 -NRM 10860 -NRN1 10861 -NRN1L 10862 -NRP1 10863 -NRP2 10864 -NRROS 10865 -NRSN1 10866 -NRSN2 10867 -NRTN 10868 -NRXN1 10869 -NRXN2 10870 -NRXN3 10871 -NSA2 10872 -NSD1 10873 -NSD2 10874 -NSD3 10875 -NSDHL 10876 -NSF 10877 -NSFL1C 10878 -NSG1 10879 -NSG2 10880 -NSL1 10881 -NSMAF 10882 -NSMCE1 10883 -NSMCE2 10884 -NSMCE3 10885 -NSMCE4A 10886 -NSMF 10887 -NSRP1 10888 -NSUN2 10889 -NSUN3 10890 -NSUN4 10891 -NSUN5 10892 -NSUN6 10893 -NSUN7 10894 -NT5C 10895 -NT5C1A 10896 -NT5C1B 10897 -NT5C2 10898 -NT5C3A 10899 -NT5C3B 10900 -NT5DC1 10901 -NT5DC2 10902 -NT5DC3 10903 -NT5DC4 10904 -NT5E 10905 -NT5M 10906 -NTAN1 10907 -NTAQ1 10908 -NTF3 10909 -NTF4 10910 -NTHL1 10911 -NTM 10912 -NTMT1 10913 -NTMT2 10914 -NTN1 10915 -NTN3 10916 -NTN4 10917 -NTN5 10918 -NTNG1 10919 -NTNG2 10920 -NTPCR 10921 -NTRK1 10922 -NTRK2 10923 -NTRK3 10924 -NTS 10925 -NTSR1 10926 -NTSR2 10927 -NUAK1 10928 -NUAK2 10929 -NUB1 10930 -NUBP1 10931 -NUBP2 10932 -NUBPL 10933 -NUCB1 10934 -NUCB2 10935 -NUCKS1 10936 -NUDC 10937 -NUDCD1 10938 -NUDCD2 10939 -NUDCD3 10940 -NUDT1 10941 -NUDT10 10942 -NUDT11 10943 -NUDT12 10944 -NUDT13 10945 -NUDT14 10946 -NUDT15 10947 -NUDT16 10948 -NUDT16L1 10949 -NUDT17 10950 -NUDT18 10951 -NUDT19 10952 -NUDT2 10953 -NUDT21 10954 -NUDT22 10955 -NUDT3 10956 -NUDT4 10957 -NUDT4B 10958 -NUDT5 10959 -NUDT6 10960 -NUDT7 10961 -NUDT8 10962 -NUDT9 10963 -NUF2 10964 -NUFIP1 10965 -NUFIP2 10966 -NUGGC 10967 -NUMA1 10968 -NUMB 10969 -NUMBL 10970 -NUP107 10971 -NUP133 10972 -NUP153 10973 -NUP155 10974 -NUP160 10975 -NUP188 10976 -NUP205 10977 -NUP210 10978 -NUP210L 10979 -NUP214 10980 -NUP35 10981 -NUP37 10982 -NUP42 10983 -NUP43 10984 -NUP50 10985 -NUP54 10986 -NUP58 10987 -NUP62 10988 -NUP62CL 10989 -NUP85 10990 -NUP88 10991 -NUP93 10992 -NUP98 10993 -NUPR1 10994 -NUPR2 10995 -NUS1 10996 -NUSAP1 10997 -NUTF2 10998 -NUTM1 10999 -NUTM2A 11000 -NUTM2B 11001 -NUTM2D 11002 -NUTM2E 11003 -NUTM2F 11004 -NUTM2G 11005 -NVL 11006 -NWD1 11007 -NWD2 11008 -NXF1 11009 -NXF2 11010 -NXF2B 11011 -NXF3 11012 -NXF5 11013 -NXN 11014 -NXNL1 11015 -NXNL2 11016 -NXPE1 11017 -NXPE2 11018 -NXPE3 11019 -NXPE4 11020 -NXPH1 11021 -NXPH2 11022 -NXPH3 11023 -NXPH4 11024 -NXT1 11025 -NXT2 11026 -NYAP1 11027 -NYAP2 11028 -NYNRIN 11029 -NYX 11030 -OAF 11031 -OARD1 11032 -OAS1 11033 -OAS2 11034 -OAS3 11035 -OASL 11036 -OAT 11037 -OAZ1 11038 -OAZ2 11039 -OAZ3 11040 -OBI1 11041 -OBP2A 11042 -OBP2B 11043 -OBSCN 11044 -OBSL1 11045 -OC90 11046 -OCA2 11047 -OCEL1 11048 -OCIAD1 11049 -OCIAD2 11050 -OCLN 11051 -OCM 11052 -OCM2 11053 -OCRL 11054 -OCSTAMP 11055 -ODAD1 11056 -ODAD2 11057 -ODAD3 11058 -ODAD4 11059 -ODAM 11060 -ODAPH 11061 -ODC1 11062 -ODF1 11063 -ODF2 11064 -ODF2L 11065 -ODF3 11066 -ODF3B 11067 -ODF3L1 11068 -ODF3L2 11069 -ODF4 11070 -ODR4 11071 -OFD1 11072 -OGA 11073 -OGDH 11074 -OGDHL 11075 -OGFOD1 11076 -OGFOD2 11077 -OGFOD3 11078 -OGFR 11079 -OGFRL1 11080 -OGG1 11081 -OGN 11082 -OGT 11083 -OIP5 11084 -OIT3 11085 -OLA1 11086 -OLAH 11087 -OLFM1 11088 -OLFM2 11089 -OLFM3 11090 -OLFM4 11091 -OLFML1 11092 -OLFML2A 11093 -OLFML2B 11094 -OLFML3 11095 -OLIG1 11096 -OLIG2 11097 -OLIG3 11098 -OLR1 11099 -OMA1 11100 -OMD 11101 -OMG 11102 -OMP 11103 -ONECUT1 11104 -ONECUT2 11105 -ONECUT3 11106 -OOEP 11107 -OOSP1 11108 -OOSP2 11109 -OOSP3 11110 -OOSP4A 11111 -OOSP4B 11112 -OPA1 11113 -OPA3 11114 -OPALIN 11115 -OPCML 11116 -OPHN1 11117 -OPLAH 11118 -OPN1LW 11119 -OPN1MW 11120 -OPN1MW2 11121 -OPN1MW3 11122 -OPN1SW 11123 -OPN3 11124 -OPN4 11125 -OPN5 11126 -OPRD1 11127 -OPRK1 11128 -OPRL1 11129 -OPRM1 11130 -OPRPN 11131 -OPTC 11132 -OPTN 11133 -OR10A2 11134 -OR10A3 11135 -OR10A4 11136 -OR10A5 11137 -OR10A6 11138 -OR10A7 11139 -OR10AC1 11140 -OR10AD1 11141 -OR10AG1 11142 -OR10C1 11143 -OR10D3 11144 -OR10G2 11145 -OR10G3 11146 -OR10G4 11147 -OR10G6 11148 -OR10G7 11149 -OR10G8 11150 -OR10G9 11151 -OR10H1 11152 -OR10H2 11153 -OR10H3 11154 -OR10H4 11155 -OR10H5 11156 -OR10J1 11157 -OR10J3 11158 -OR10J4 11159 -OR10J5 11160 -OR10K1 11161 -OR10K2 11162 -OR10P1 11163 -OR10Q1 11164 -OR10R2 11165 -OR10S1 11166 -OR10T2 11167 -OR10V1 11168 -OR10W1 11169 -OR10X1 11170 -OR10Z1 11171 -OR11A1 11172 -OR11G2 11173 -OR11H1 11174 -OR11H12 11175 -OR11H2 11176 -OR11H4 11177 -OR11H6 11178 -OR11H7 11179 -OR11L1 11180 -OR12D1 11181 -OR12D2 11182 -OR12D3 11183 -OR13A1 11184 -OR13C2 11185 -OR13C3 11186 -OR13C4 11187 -OR13C5 11188 -OR13C7 11189 -OR13C8 11190 -OR13C9 11191 -OR13D1 11192 -OR13F1 11193 -OR13G1 11194 -OR13H1 11195 -OR13J1 11196 -OR14A16 11197 -OR14A2 11198 -OR14C36 11199 -OR14I1 11200 -OR14J1 11201 -OR14K1 11202 -OR1A1 11203 -OR1A2 11204 -OR1B1 11205 -OR1C1 11206 -OR1D2 11207 -OR1D4 11208 -OR1D5 11209 -OR1E1 11210 -OR1E2 11211 -OR1E3 11212 -OR1F1 11213 -OR1G1 11214 -OR1I1 11215 -OR1J1 11216 -OR1J2 11217 -OR1J4 11218 -OR1K1 11219 -OR1L1 11220 -OR1L3 11221 -OR1L4 11222 -OR1L6 11223 -OR1L8 11224 -OR1M1 11225 -OR1N1 11226 -OR1N2 11227 -OR1P1 11228 -OR1Q1 11229 -OR1S1 11230 -OR1S2 11231 -OR2A1 11232 -OR2A12 11233 -OR2A14 11234 -OR2A2 11235 -OR2A25 11236 -OR2A4 11237 -OR2A42 11238 -OR2A5 11239 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11388 -OR52D1 11389 -OR52E1 11390 -OR52E2 11391 -OR52E4 11392 -OR52E5 11393 -OR52E6 11394 -OR52E8 11395 -OR52H1 11396 -OR52I1 11397 -OR52I2 11398 -OR52J3 11399 -OR52K1 11400 -OR52K2 11401 -OR52L1 11402 -OR52M1 11403 -OR52N1 11404 -OR52N2 11405 -OR52N4 11406 -OR52N5 11407 -OR52P1 11408 -OR52R1 11409 -OR52W1 11410 -OR56A1 11411 -OR56A3 11412 -OR56A4 11413 -OR56A5 11414 -OR56B1 11415 -OR56B4 11416 -OR5A1 11417 -OR5A2 11418 -OR5AC1 11419 -OR5AC2 11420 -OR5AK2 11421 -OR5AL1 11422 -OR5AN1 11423 -OR5AP2 11424 -OR5AR1 11425 -OR5AS1 11426 -OR5AU1 11427 -OR5B12 11428 -OR5B17 11429 -OR5B2 11430 -OR5B21 11431 -OR5B3 11432 -OR5C1 11433 -OR5D13 11434 -OR5D14 11435 -OR5D16 11436 -OR5D18 11437 -OR5F1 11438 -OR5G3 11439 -OR5H1 11440 -OR5H14 11441 -OR5H15 11442 -OR5H2 11443 -OR5H6 11444 -OR5H8 11445 -OR5I1 11446 -OR5J2 11447 -OR5K1 11448 -OR5K2 11449 -OR5K3 11450 -OR5K4 11451 -OR5L1 11452 -OR5L2 11453 -OR5M1 11454 -OR5M10 11455 -OR5M11 11456 -OR5M3 11457 -OR5M8 11458 -OR5M9 11459 -OR5P2 11460 -OR5P3 11461 -OR5T1 11462 -OR5T2 11463 -OR5T3 11464 -OR5V1 11465 -OR5W2 11466 -OR6A2 11467 -OR6B1 11468 -OR6B2 11469 -OR6B3 11470 -OR6C1 11471 -OR6C2 11472 -OR6C3 11473 -OR6C4 11474 -OR6C6 11475 -OR6C65 11476 -OR6C68 11477 -OR6C70 11478 -OR6C74 11479 -OR6C75 11480 -OR6C76 11481 -OR6F1 11482 -OR6J1 11483 -OR6K2 11484 -OR6K3 11485 -OR6K6 11486 -OR6M1 11487 -OR6N1 11488 -OR6N2 11489 -OR6P1 11490 -OR6Q1 11491 -OR6S1 11492 -OR6T1 11493 -OR6V1 11494 -OR6X1 11495 -OR6Y1 11496 -OR7A10 11497 -OR7A17 11498 -OR7A5 11499 -OR7C1 11500 -OR7C2 11501 -OR7D2 11502 -OR7D4 11503 -OR7E24 11504 -OR7G1 11505 -OR7G2 11506 -OR7G3 11507 -OR8A1 11508 -OR8B12 11509 -OR8B2 11510 -OR8B3 11511 -OR8B4 11512 -OR8B8 11513 -OR8D1 11514 -OR8D2 11515 -OR8D4 11516 -OR8G1 11517 -OR8G5 11518 -OR8H1 11519 -OR8H2 11520 -OR8H3 11521 -OR8I2 11522 -OR8J1 11523 -OR8J2 11524 -OR8J3 11525 -OR8K1 11526 -OR8K3 11527 -OR8K5 11528 -OR8S1 11529 -OR8U1 11530 -OR8U3 11531 -OR9A2 11532 -OR9A4 11533 -OR9G1 11534 -OR9G4 11535 -OR9I1 11536 -OR9K2 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11614 -OVOL3 11615 -OXA1L 11616 -OXCT1 11617 -OXCT2 11618 -OXER1 11619 -OXGR1 11620 -OXLD1 11621 -OXNAD1 11622 -OXR1 11623 -OXSM 11624 -OXSR1 11625 -OXT 11626 -OXTR 11627 -P2RX1 11628 -P2RX2 11629 -P2RX3 11630 -P2RX4 11631 -P2RX5 11632 -P2RX6 11633 -P2RX7 11634 -P2RY1 11635 -P2RY10 11636 -P2RY11 11637 -P2RY12 11638 -P2RY13 11639 -P2RY14 11640 -P2RY2 11641 -P2RY4 11642 -P2RY6 11643 -P2RY8 11644 -P3H1 11645 -P3H2 11646 -P3H3 11647 -P3H4 11648 -P3R3URF 11649 -P4HA1 11650 -P4HA2 11651 -P4HA3 11652 -P4HB 11653 -P4HTM 11654 -PA2G4 11655 -PAAF1 11656 -PABIR1 11657 -PABIR2 11658 -PABIR3 11659 -PABPC1 11660 -PABPC1L 11661 -PABPC1L2A 11662 -PABPC1L2B 11663 -PABPC3 11664 -PABPC4 11665 -PABPC4L 11666 -PABPC5 11667 -PABPN1 11668 -PABPN1L 11669 -PACC1 11670 -PACRG 11671 -PACRGL 11672 -PACS1 11673 -PACS2 11674 -PACSIN1 11675 -PACSIN2 11676 -PACSIN3 11677 -PADI1 11678 -PADI2 11679 -PADI3 11680 -PADI4 11681 -PADI6 11682 -PAEP 11683 -PAF1 11684 -PAFAH1B1 11685 -PAFAH1B2 11686 -PAFAH1B3 11687 -PAFAH2 11688 -PAG1 11689 -PAGE1 11690 -PAGE2 11691 -PAGE2B 11692 -PAGE3 11693 -PAGE4 11694 -PAGE5 11695 -PAGR1 11696 -PAH 11697 -PAICS 11698 -PAIP1 11699 -PAIP2 11700 -PAIP2B 11701 -PAK1 11702 -PAK1IP1 11703 -PAK2 11704 -PAK3 11705 -PAK4 11706 -PAK5 11707 -PAK6 11708 -PALB2 11709 -PALD1 11710 -PALLD 11711 -PALM 11712 -PALM2AKAP2 11713 -PALM3 11714 -PALMD 11715 -PALS1 11716 -PALS2 11717 -PAM 11718 -PAM16 11719 -PAMR1 11720 -PAN2 11721 -PAN3 11722 -PANK1 11723 -PANK2 11724 -PANK3 11725 -PANK4 11726 -PANO1 11727 -PANX1 11728 -PANX2 11729 -PANX3 11730 -PAOX 11731 -PAPLN 11732 -PAPOLA 11733 -PAPOLB 11734 -PAPOLG 11735 -PAPPA 11736 -PAPPA2 11737 -PAPSS1 11738 -PAPSS2 11739 -PAQR3 11740 -PAQR4 11741 -PAQR5 11742 -PAQR6 11743 -PAQR7 11744 -PAQR8 11745 -PAQR9 11746 -PARD3 11747 -PARD3B 11748 -PARD6A 11749 -PARD6B 11750 -PARD6G 11751 -PARG 11752 -PARK7 11753 -PARL 11754 -PARM1 11755 -PARN 11756 -PARP1 11757 -PARP10 11758 -PARP11 11759 -PARP12 11760 -PARP14 11761 -PARP15 11762 -PARP16 11763 -PARP2 11764 -PARP3 11765 -PARP4 11766 -PARP6 11767 -PARP8 11768 -PARP9 11769 -PARPBP 11770 -PARS2 11771 -PARVA 11772 -PARVB 11773 -PARVG 11774 -PASD1 11775 -PASK 11776 -PATE1 11777 -PATE2 11778 -PATE3 11779 -PATE4 11780 -PATJ 11781 -PATL1 11782 -PATL2 11783 -PATZ1 11784 -PAWR 11785 -PAX1 11786 -PAX2 11787 -PAX3 11788 -PAX4 11789 -PAX5 11790 -PAX6 11791 -PAX7 11792 -PAX8 11793 -PAX9 11794 -PAXBP1 11795 -PAXIP1 11796 -PAXX 11797 -PBDC1 11798 -PBK 11799 -PBLD 11800 -PBOV1 11801 -PBRM1 11802 -PBX1 11803 -PBX2 11804 -PBX3 11805 -PBX4 11806 -PBXIP1 11807 -PC 11808 -PCARE 11809 -PCBD1 11810 -PCBD2 11811 -PCBP1 11812 -PCBP2 11813 -PCBP3 11814 -PCBP4 11815 -PCCA 11816 -PCCB 11817 -PCDH1 11818 -PCDH10 11819 -PCDH11X 11820 -PCDH11Y 11821 -PCDH12 11822 -PCDH15 11823 -PCDH17 11824 -PCDH18 11825 -PCDH19 11826 -PCDH20 11827 -PCDH7 11828 -PCDH8 11829 -PCDH9 11830 -PCDHA@ 11831 -PCDHB@ 11832 -PCDHG@ 11833 -PCED1A 11834 -PCED1B 11835 -PCF11 11836 -PCGF1 11837 -PCGF2 11838 -PCGF3 11839 -PCGF5 11840 -PCGF6 11841 -PCID2 11842 -PCIF1 11843 -PCK1 11844 -PCK2 11845 -PCLAF 11846 -PCLO 11847 -PCM1 11848 -PCMT1 11849 -PCMTD1 11850 -PCMTD2 11851 -PCNA 11852 -PCNP 11853 -PCNT 11854 -PCNX1 11855 -PCNX2 11856 -PCNX3 11857 -PCNX4 11858 -PCOLCE 11859 -PCOLCE2 11860 -PCP2 11861 -PCP4 11862 -PCP4L1 11863 -PCSK1 11864 -PCSK1N 11865 -PCSK2 11866 -PCSK4 11867 -PCSK5 11868 -PCSK6 11869 -PCSK7 11870 -PCSK9 11871 -PCTP 11872 -PCYOX1 11873 -PCYOX1L 11874 -PCYT1A 11875 -PCYT1B 11876 -PCYT2 11877 -PDAP1 11878 -PDC 11879 -PDCD1 11880 -PDCD10 11881 -PDCD11 11882 -PDCD1LG2 11883 -PDCD2 11884 -PDCD2L 11885 -PDCD4 11886 -PDCD5 11887 -PDCD6 11888 -PDCD6IP 11889 -PDCD7 11890 -PDCL 11891 -PDCL2 11892 -PDCL3 11893 -PDE10A 11894 -PDE11A 11895 -PDE12 11896 -PDE1A 11897 -PDE1B 11898 -PDE1C 11899 -PDE2A 11900 -PDE3A 11901 -PDE3B 11902 -PDE4A 11903 -PDE4B 11904 -PDE4C 11905 -PDE4D 11906 -PDE4DIP 11907 -PDE5A 11908 -PDE6A 11909 -PDE6B 11910 -PDE6C 11911 -PDE6D 11912 -PDE6G 11913 -PDE6H 11914 -PDE7A 11915 -PDE7B 11916 -PDE8A 11917 -PDE8B 11918 -PDE9A 11919 -PDF 11920 -PDGFA 11921 -PDGFB 11922 -PDGFC 11923 -PDGFD 11924 -PDGFRA 11925 -PDGFRB 11926 -PDGFRL 11927 -PDHA1 11928 -PDHA2 11929 -PDHB 11930 -PDHX 11931 -PDIA2 11932 -PDIA3 11933 -PDIA4 11934 -PDIA5 11935 -PDIA6 11936 -PDIK1L 11937 -PDILT 11938 -PDK1 11939 -PDK2 11940 -PDK3 11941 -PDK4 11942 -PDLIM1 11943 -PDLIM2 11944 -PDLIM3 11945 -PDLIM4 11946 -PDLIM5 11947 -PDLIM7 11948 -PDP1 11949 -PDP2 11950 -PDPK1 11951 -PDPN 11952 -PDPR 11953 -PDRG1 11954 -PDS5A 11955 -PDS5B 11956 -PDSS1 11957 -PDSS2 11958 -PDX1 11959 -PDXDC1 11960 -PDXK 11961 -PDXP 11962 -PDYN 11963 -PDZD11 11964 -PDZD2 11965 -PDZD3 11966 -PDZD4 11967 -PDZD7 11968 -PDZD8 11969 -PDZD9 11970 -PDZK1 11971 -PDZK1IP1 11972 -PDZRN3 11973 -PDZRN4 11974 -PEA15 11975 -PEAK1 11976 -PEAK3 11977 -PEAR1 11978 -PEBP1 11979 -PEBP4 11980 -PECAM1 11981 -PECR 11982 -PEDS1 11983 -PEF1 11984 -PEG10 11985 -PEG3 11986 -PELI1 11987 -PELI2 11988 -PELI3 11989 -PELO 11990 -PELP1 11991 -PEMT 11992 -PENK 11993 -PEPD 11994 -PER1 11995 -PER2 11996 -PER3 11997 -PERCC1 11998 -PERM1 11999 -PERP 12000 -PES1 12001 -PET100 12002 -PET117 12003 -PEX1 12004 -PEX10 12005 -PEX11A 12006 -PEX11B 12007 -PEX11G 12008 -PEX12 12009 -PEX13 12010 -PEX14 12011 -PEX16 12012 -PEX19 12013 -PEX2 12014 -PEX26 12015 -PEX3 12016 -PEX5 12017 -PEX5L 12018 -PEX6 12019 -PEX7 12020 -PF4 12021 -PF4V1 12022 -PFAS 12023 -PFDN1 12024 -PFDN2 12025 -PFDN4 12026 -PFDN5 12027 -PFDN6 12028 -PFKFB1 12029 -PFKFB2 12030 -PFKFB3 12031 -PFKFB4 12032 -PFKL 12033 -PFKM 12034 -PFKP 12035 -PFN1 12036 -PFN2 12037 -PFN3 12038 -PFN4 12039 -PGA3 12040 -PGA4 12041 -PGA5 12042 -PGAM1 12043 -PGAM2 12044 -PGAM4 12045 -PGAM5 12046 -PGAP1 12047 -PGAP2 12048 -PGAP3 12049 -PGAP4 12050 -PGAP6 12051 -PGBD1 12052 -PGBD2 12053 -PGBD3 12054 -PGBD4 12055 -PGBD5 12056 -PGC 12057 -PGD 12058 -PGF 12059 -PGGHG 12060 -PGGT1B 12061 -PGK1 12062 -PGK2 12063 -PGLS 12064 -PGLYRP1 12065 -PGLYRP2 12066 -PGLYRP3 12067 -PGLYRP4 12068 -PGM1 12069 -PGM2 12070 -PGM2L1 12071 -PGM3 12072 -PGM5 12073 -PGP 12074 -PGPEP1 12075 -PGPEP1L 12076 -PGR 12077 -PGRMC1 12078 -PGRMC2 12079 -PGS1 12080 -PHACTR1 12081 -PHACTR2 12082 -PHACTR3 12083 -PHACTR4 12084 -PHAF1 12085 -PHAX 12086 -PHB1 12087 -PHB2 12088 -PHC1 12089 -PHC2 12090 -PHC3 12091 -PHETA1 12092 -PHETA2 12093 -PHEX 12094 -PHF1 12095 -PHF10 12096 -PHF11 12097 -PHF12 12098 -PHF13 12099 -PHF14 12100 -PHF19 12101 -PHF2 12102 -PHF20 12103 -PHF20L1 12104 -PHF21A 12105 -PHF21B 12106 -PHF23 12107 -PHF24 12108 -PHF3 12109 -PHF5A 12110 -PHF6 12111 -PHF7 12112 -PHF8 12113 -PHGDH 12114 -PHGR1 12115 -PHIP 12116 -PHKA1 12117 -PHKA2 12118 -PHKB 12119 -PHKG1 12120 -PHKG2 12121 -PHLDA1 12122 -PHLDA2 12123 -PHLDA3 12124 -PHLDB1 12125 -PHLDB2 12126 -PHLDB3 12127 -PHLPP1 12128 -PHLPP2 12129 -PHOSPHO1 12130 -PHOSPHO2 12131 -PHOX2A 12132 -PHOX2B 12133 -PHPT1 12134 -PHRF1 12135 -PHTF1 12136 -PHTF2 12137 -PHYH 12138 -PHYHD1 12139 -PHYHIP 12140 -PHYHIPL 12141 -PHYKPL 12142 -PI15 12143 -PI16 12144 -PI3 12145 -PI4K2A 12146 -PI4K2B 12147 -PI4KA 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-PTPRCAP 13183 -PTPRD 13184 -PTPRE 13185 -PTPRF 13186 -PTPRG 13187 -PTPRH 13188 -PTPRJ 13189 -PTPRK 13190 -PTPRM 13191 -PTPRN 13192 -PTPRN2 13193 -PTPRO 13194 -PTPRQ 13195 -PTPRR 13196 -PTPRS 13197 -PTPRT 13198 -PTPRU 13199 -PTPRZ1 13200 -PTRH1 13201 -PTRH2 13202 -PTRHD1 13203 -PTS 13204 -PTTG1 13205 -PTTG1IP 13206 -PTTG1IP2 13207 -PTTG2 13208 -PTX3 13209 -PTX4 13210 -PUDP 13211 -PUF60 13212 -PUM1 13213 -PUM2 13214 -PUM3 13215 -PURA 13216 -PURB 13217 -PURG 13218 -PUS1 13219 -PUS10 13220 -PUS3 13221 -PUS7 13222 -PUS7L 13223 -PUSL1 13224 -PVALB 13225 -PVALEF 13226 -PVR 13227 -PVRIG 13228 -PWP1 13229 -PWP2 13230 -PWWP2A 13231 -PWWP2B 13232 -PWWP3A 13233 -PWWP3B 13234 -PXDC1 13235 -PXDN 13236 -PXDNL 13237 -PXK 13238 -PXMP2 13239 -PXMP4 13240 -PXN 13241 -PXT1 13242 -PXYLP1 13243 -PYCARD 13244 -PYCR1 13245 -PYCR2 13246 -PYCR3 13247 -PYDC1 13248 -PYDC2 13249 -PYDC5 13250 -PYGB 13251 -PYGL 13252 -PYGM 13253 -PYGO1 13254 -PYGO2 13255 -PYHIN1 13256 -PYM1 13257 -PYROXD1 13258 -PYROXD2 13259 -PYURF 13260 -PYY 13261 -PZP 13262 -QARS1 13263 -QDPR 13264 -QKI 13265 -QPCT 13266 -QPCTL 13267 -QPRT 13268 -QRFP 13269 -QRFPR 13270 -QRICH1 13271 -QRICH2 13272 -QRSL1 13273 -QSER1 13274 -QSOX1 13275 -QSOX2 13276 -QTRT1 13277 -QTRT2 13278 -R3HCC1 13279 -R3HCC1L 13280 -R3HDM1 13281 -R3HDM2 13282 -R3HDM4 13283 -R3HDML 13284 -RAB10 13285 -RAB11A 13286 -RAB11B 13287 -RAB11FIP1 13288 -RAB11FIP2 13289 -RAB11FIP3 13290 -RAB11FIP4 13291 -RAB11FIP5 13292 -RAB12 13293 -RAB13 13294 -RAB14 13295 -RAB15 13296 -RAB17 13297 -RAB18 13298 -RAB19 13299 -RAB1A 13300 -RAB1B 13301 -RAB20 13302 -RAB21 13303 -RAB22A 13304 -RAB23 13305 -RAB24 13306 -RAB25 13307 -RAB26 13308 -RAB27A 13309 -RAB27B 13310 -RAB28 13311 -RAB29 13312 -RAB2A 13313 -RAB2B 13314 -RAB30 13315 -RAB31 13316 -RAB32 13317 -RAB33A 13318 -RAB33B 13319 -RAB34 13320 -RAB35 13321 -RAB36 13322 -RAB37 13323 -RAB38 13324 -RAB39A 13325 -RAB39B 13326 -RAB3A 13327 -RAB3B 13328 -RAB3C 13329 -RAB3D 13330 -RAB3GAP1 13331 -RAB3GAP2 13332 -RAB3IL1 13333 -RAB3IP 13334 -RAB40A 13335 -RAB40AL 13336 -RAB40B 13337 -RAB40C 13338 -RAB41 13339 -RAB42 13340 -RAB43 13341 -RAB44 13342 -RAB4A 13343 -RAB4B 13344 -RAB5A 13345 -RAB5B 13346 -RAB5C 13347 -RAB5IF 13348 -RAB6A 13349 -RAB6B 13350 -RAB6C 13351 -RAB6D 13352 -RAB7A 13353 -RAB7B 13354 -RAB8A 13355 -RAB8B 13356 -RAB9A 13357 -RAB9B 13358 -RABAC1 13359 -RABEP1 13360 -RABEP2 13361 -RABEPK 13362 -RABGAP1 13363 -RABGAP1L 13364 -RABGEF1 13365 -RABGGTA 13366 -RABGGTB 13367 -RABIF 13368 -RABL2A 13369 -RABL2B 13370 -RABL3 13371 -RABL6 13372 -RAC1 13373 -RAC2 13374 -RAC3 13375 -RACGAP1 13376 -RACK1 13377 -RAD1 13378 -RAD17 13379 -RAD18 13380 -RAD21 13381 -RAD21L1 13382 -RAD23A 13383 -RAD23B 13384 -RAD50 13385 -RAD51 13386 -RAD51AP1 13387 -RAD51AP2 13388 -RAD51B 13389 -RAD51C 13390 -RAD51D 13391 -RAD52 13392 -RAD54B 13393 -RAD54L 13394 -RAD54L2 13395 -RAD9A 13396 -RAD9B 13397 -RADIL 13398 -RADX 13399 -RAE1 13400 -RAET1E 13401 -RAET1G 13402 -RAET1L 13403 -RAF1 13404 -RAG1 13405 -RAG2 13406 -RAI1 13407 -RAI14 13408 -RAI2 13409 -RALA 13410 -RALB 13411 -RALBP1 13412 -RALGAPA1 13413 -RALGAPA2 13414 -RALGAPB 13415 -RALGDS 13416 -RALGPS1 13417 -RALGPS2 13418 -RALY 13419 -RALYL 13420 -RAMAC 13421 -RAMP1 13422 -RAMP2 13423 -RAMP3 13424 -RAN 13425 -RANBP1 13426 -RANBP10 13427 -RANBP17 13428 -RANBP2 13429 -RANBP3 13430 -RANBP3L 13431 -RANBP6 13432 -RANBP9 13433 -RANGAP1 13434 -RANGRF 13435 -RAP1A 13436 -RAP1B 13437 -RAP1GAP 13438 -RAP1GAP2 13439 -RAP1GDS1 13440 -RAP2A 13441 -RAP2B 13442 -RAP2C 13443 -RAPGEF1 13444 -RAPGEF2 13445 -RAPGEF3 13446 -RAPGEF4 13447 -RAPGEF5 13448 -RAPGEF6 13449 -RAPGEFL1 13450 -RAPH1 13451 -RAPSN 13452 -RARA 13453 -RARB 13454 -RARG 13455 -RARRES1 13456 -RARRES2 13457 -RARS1 13458 -RARS2 13459 -RASA1 13460 -RASA2 13461 -RASA3 13462 -RASA4 13463 -RASA4B 13464 -RASAL1 13465 -RASAL2 13466 -RASAL3 13467 -RASD1 13468 -RASD2 13469 -RASEF 13470 -RASGEF1A 13471 -RASGEF1B 13472 -RASGEF1C 13473 -RASGRF1 13474 -RASGRF2 13475 -RASGRP1 13476 -RASGRP2 13477 -RASGRP3 13478 -RASGRP4 13479 -RASIP1 13480 -RASL10A 13481 -RASL10B 13482 -RASL11A 13483 -RASL11B 13484 -RASL12 13485 -RASSF1 13486 -RASSF10 13487 -RASSF2 13488 -RASSF3 13489 -RASSF4 13490 -RASSF5 13491 -RASSF6 13492 -RASSF7 13493 -RASSF8 13494 -RASSF9 13495 -RAVER1 13496 -RAVER2 13497 -RAX 13498 -RAX2 13499 -RB1 13500 -RB1CC1 13501 -RBAK 13502 -RBBP4 13503 -RBBP5 13504 -RBBP6 13505 -RBBP7 13506 -RBBP8 13507 -RBBP8NL 13508 -RBBP9 13509 -RBCK1 13510 -RBFA 13511 -RBFOX1 13512 -RBFOX2 13513 -RBFOX3 13514 -RBIS 13515 -RBKS 13516 -RBL1 13517 -RBL2 13518 -RBM10 13519 -RBM11 13520 -RBM12 13521 -RBM12B 13522 -RBM14 13523 -RBM15 13524 -RBM15B 13525 -RBM17 13526 -RBM18 13527 -RBM19 13528 -RBM20 13529 -RBM22 13530 -RBM23 13531 -RBM24 13532 -RBM25 13533 -RBM26 13534 -RBM27 13535 -RBM28 13536 -RBM3 13537 -RBM33 13538 -RBM34 13539 -RBM38 13540 -RBM39 13541 -RBM4 13542 -RBM41 13543 -RBM42 13544 -RBM43 13545 -RBM44 13546 -RBM45 13547 -RBM46 13548 -RBM47 13549 -RBM48 13550 -RBM4B 13551 -RBM5 13552 -RBM6 13553 -RBM7 13554 -RBM8A 13555 -RBMS1 13556 -RBMS2 13557 -RBMS3 13558 -RBMX 13559 -RBMX2 13560 -RBMXL1 13561 -RBMXL2 13562 -RBMXL3 13563 -RBMY1A1 13564 -RBMY1B 13565 -RBMY1C 13566 -RBMY1D 13567 -RBMY1E 13568 -RBMY1F 13569 -RBMY1J 13570 -RBP1 13571 -RBP2 13572 -RBP3 13573 -RBP4 13574 -RBP5 13575 -RBP7 13576 -RBPJ 13577 -RBPJL 13578 -RBPMS 13579 -RBPMS2 13580 -RBSN 13581 -RBX1 13582 -RC3H1 13583 -RC3H2 13584 -RCAN1 13585 -RCAN2 13586 -RCAN3 13587 -RCBTB1 13588 -RCBTB2 13589 -RCC1 13590 -RCC1L 13591 -RCC2 13592 -RCCD1 13593 -RCE1 13594 -RCHY1 13595 -RCL1 13596 -RCN1 13597 -RCN2 13598 -RCN3 13599 -RCOR1 13600 -RCOR2 13601 -RCOR3 13602 -RCSD1 13603 -RCVRN 13604 -RD3 13605 -RD3L 13606 -RDH10 13607 -RDH11 13608 -RDH12 13609 -RDH13 13610 -RDH14 13611 -RDH16 13612 -RDH5 13613 -RDH8 13614 -RDM1 13615 -RDX 13616 -REC114 13617 -REC8 13618 -RECK 13619 -RECQL 13620 -RECQL4 13621 -RECQL5 13622 -REELD1 13623 -REEP1 13624 -REEP2 13625 -REEP3 13626 -REEP4 13627 -REEP5 13628 -REEP6 13629 -REG1A 13630 -REG1B 13631 -REG3A 13632 -REG3G 13633 -REG4 13634 -REL 13635 -RELA 13636 -RELB 13637 -RELCH 13638 -RELL1 13639 -RELL2 13640 -RELN 13641 -RELT 13642 -REM1 13643 -REM2 13644 -REN 13645 -RENBP 13646 -REP15 13647 -REPIN1 13648 -REPS1 13649 -REPS2 13650 -RER1 13651 -RERE 13652 -RERG 13653 -RERGL 13654 -RESF1 13655 -RESP18 13656 -REST 13657 -RET 13658 -RETN 13659 -RETNLB 13660 -RETREG1 13661 -RETREG2 13662 -RETREG3 13663 -RETSAT 13664 -REV1 13665 -REV3L 13666 -REX1BD 13667 -REXO1 13668 -REXO2 13669 -REXO4 13670 -REXO5 13671 -RFC1 13672 -RFC2 13673 -RFC3 13674 -RFC4 13675 -RFC5 13676 -RFESD 13677 -RFFL 13678 -RFK 13679 -RFLNA 13680 -RFLNB 13681 -RFNG 13682 -RFPL1 13683 -RFPL2 13684 -RFPL3 13685 -RFPL4A 13686 -RFPL4AL1 13687 -RFPL4B 13688 -RFT1 13689 -RFTN1 13690 -RFTN2 13691 -RFWD3 13692 -RFX1 13693 -RFX2 13694 -RFX3 13695 -RFX4 13696 -RFX5 13697 -RFX6 13698 -RFX7 13699 -RFX8 13700 -RFXANK 13701 -RFXAP 13702 -RGCC 13703 -RGL1 13704 -RGL2 13705 -RGL3 13706 -RGL4 13707 -RGMA 13708 -RGMB 13709 -RGN 13710 -RGP1 13711 -RGPD1 13712 -RGPD2 13713 -RGPD3 13714 -RGPD4 13715 -RGPD5 13716 -RGPD6 13717 -RGPD8 13718 -RGR 13719 -RGS1 13720 -RGS10 13721 -RGS11 13722 -RGS12 13723 -RGS13 13724 -RGS14 13725 -RGS16 13726 -RGS17 13727 -RGS18 13728 -RGS19 13729 -RGS2 13730 -RGS20 13731 -RGS21 13732 -RGS22 13733 -RGS3 13734 -RGS4 13735 -RGS5 13736 -RGS6 13737 -RGS7 13738 -RGS7BP 13739 -RGS8 13740 -RGS9 13741 -RGS9BP 13742 -RGSL1 13743 -RHAG 13744 -RHBDD1 13745 -RHBDD2 13746 -RHBDD3 13747 -RHBDF1 13748 -RHBDF2 13749 -RHBDL1 13750 -RHBDL2 13751 -RHBDL3 13752 -RHBG 13753 -RHCE 13754 -RHCG 13755 -RHD 13756 -RHEB 13757 -RHEBL1 13758 -RHEX 13759 -RHNO1 13760 -RHO 13761 -RHOA 13762 -RHOB 13763 -RHOBTB1 13764 -RHOBTB2 13765 -RHOBTB3 13766 -RHOC 13767 -RHOD 13768 -RHOF 13769 -RHOG 13770 -RHOH 13771 -RHOJ 13772 -RHOQ 13773 -RHOT1 13774 -RHOT2 13775 -RHOU 13776 -RHOV 13777 -RHOXF1 13778 -RHOXF2 13779 -RHOXF2B 13780 -RHPN1 13781 -RHPN2 13782 -RIBC1 13783 -RIBC2 13784 -RIC1 13785 -RIC3 13786 -RIC8A 13787 -RIC8B 13788 -RICTOR 13789 -RIDA 13790 -RIF1 13791 -RIIAD1 13792 -RILP 13793 -RILPL1 13794 -RILPL2 13795 -RIMBP2 13796 -RIMBP3 13797 -RIMBP3B 13798 -RIMBP3C 13799 -RIMKLA 13800 -RIMKLB 13801 -RIMOC1 13802 -RIMS1 13803 -RIMS2 13804 -RIMS3 13805 -RIMS4 13806 -RIN1 13807 -RIN2 13808 -RIN3 13809 -RING1 13810 -RINL 13811 -RINT1 13812 -RIOK1 13813 -RIOK2 13814 -RIOK3 13815 -RIOX1 13816 -RIOX2 13817 -RIPK1 13818 -RIPK2 13819 -RIPK3 13820 -RIPK4 13821 -RIPOR1 13822 -RIPOR2 13823 -RIPOR3 13824 -RIPPLY1 13825 -RIPPLY2 13826 -RIPPLY3 13827 -RIT1 13828 -RIT2 13829 -RITA1 13830 -RLBP1 13831 -RLF 13832 -RLIM 13833 -RLN1 13834 -RLN2 13835 -RLN3 13836 -RMC1 13837 -RMDN1 13838 -RMDN2 13839 -RMDN3 13840 -RMI1 13841 -RMI2 13842 -RMND1 13843 -RMND5A 13844 -RMND5B 13845 -RNASE1 13846 -RNASE10 13847 -RNASE11 13848 -RNASE12 13849 -RNASE13 13850 -RNASE2 13851 -RNASE3 13852 -RNASE4 13853 -RNASE6 13854 -RNASE7 13855 -RNASE8 13856 -RNASE9 13857 -RNASEH1 13858 -RNASEH2A 13859 -RNASEH2B 13860 -RNASEH2C 13861 -RNASEK 13862 -RNASEL 13863 -RNASET2 13864 -RND1 13865 -RND2 13866 -RND3 13867 -RNF10 13868 -RNF103 13869 -RNF11 13870 -RNF111 13871 -RNF112 13872 -RNF113A 13873 -RNF113B 13874 -RNF114 13875 -RNF115 13876 -RNF121 13877 -RNF122 13878 -RNF123 13879 -RNF125 13880 -RNF126 13881 -RNF128 13882 -RNF13 13883 -RNF130 13884 -RNF133 13885 -RNF135 13886 -RNF138 13887 -RNF139 13888 -RNF14 13889 -RNF141 13890 -RNF144A 13891 -RNF144B 13892 -RNF145 13893 -RNF146 13894 -RNF148 13895 -RNF149 13896 -RNF150 13897 -RNF151 13898 -RNF152 13899 -RNF157 13900 -RNF165 13901 -RNF166 13902 -RNF167 13903 -RNF168 13904 -RNF169 13905 -RNF17 13906 -RNF170 13907 -RNF175 13908 -RNF180 13909 -RNF181 13910 -RNF182 13911 -RNF183 13912 -RNF185 13913 -RNF186 13914 -RNF187 13915 -RNF19A 13916 -RNF19B 13917 -RNF2 13918 -RNF20 13919 -RNF207 13920 -RNF208 13921 -RNF212 13922 -RNF212B 13923 -RNF213 13924 -RNF214 13925 -RNF215 13926 -RNF216 13927 -RNF217 13928 -RNF220 13929 -RNF222 13930 -RNF223 13931 -RNF224 13932 -RNF225 13933 -RNF227 13934 -RNF24 13935 -RNF25 13936 -RNF26 13937 -RNF31 13938 -RNF32 13939 -RNF34 13940 -RNF38 13941 -RNF39 13942 -RNF4 13943 -RNF40 13944 -RNF41 13945 -RNF43 13946 -RNF44 13947 -RNF5 13948 -RNF6 13949 -RNF7 13950 -RNF8 13951 -RNFT1 13952 -RNFT2 13953 -RNGTT 13954 -RNH1 13955 -RNLS 13956 -RNMT 13957 -RNPC3 13958 -RNPEP 13959 -RNPEPL1 13960 -RNPS1 13961 -RO60 13962 -ROBO1 13963 -ROBO2 13964 -ROBO3 13965 -ROBO4 13966 -ROCK1 13967 -ROCK2 13968 -ROGDI 13969 -ROM1 13970 -ROMO1 13971 -ROPN1 13972 -ROPN1B 13973 -ROPN1L 13974 -ROR1 13975 -ROR2 13976 -RORA 13977 -RORB 13978 -RORC 13979 -ROS1 13980 -RP1 13981 -RP1L1 13982 -RP2 13983 -RP9 13984 -RPA1 13985 -RPA2 13986 -RPA3 13987 -RPA4 13988 -RPAIN 13989 -RPAP1 13990 -RPAP2 13991 -RPAP3 13992 -RPE 13993 -RPE65 13994 -RPEL1 13995 -RPF1 13996 -RPF2 13997 -RPGR 13998 -RPGRIP1 13999 -RPGRIP1L 14000 -RPH3A 14001 -RPH3AL 14002 -RPIA 14003 -RPL10 14004 -RPL10A 14005 -RPL10L 14006 -RPL11 14007 -RPL12 14008 -RPL13 14009 -RPL13A 14010 -RPL14 14011 -RPL15 14012 -RPL17 14013 -RPL18 14014 -RPL18A 14015 -RPL19 14016 -RPL21 14017 -RPL22 14018 -RPL22L1 14019 -RPL23 14020 -RPL23A 14021 -RPL24 14022 -RPL26 14023 -RPL26L1 14024 -RPL27 14025 -RPL27A 14026 -RPL28 14027 -RPL29 14028 -RPL3 14029 -RPL30 14030 -RPL31 14031 -RPL32 14032 -RPL34 14033 -RPL35 14034 -RPL35A 14035 -RPL36 14036 -RPL36A 14037 -RPL36AL 14038 -RPL37 14039 -RPL37A 14040 -RPL38 14041 -RPL39 14042 -RPL39L 14043 -RPL3L 14044 -RPL4 14045 -RPL41 14046 -RPL5 14047 -RPL6 14048 -RPL7 14049 -RPL7A 14050 -RPL7L1 14051 -RPL8 14052 -RPL9 14053 -RPLP0 14054 -RPLP1 14055 -RPLP2 14056 -RPN1 14057 -RPN2 14058 -RPP14 14059 -RPP21 14060 -RPP25 14061 -RPP25L 14062 -RPP30 14063 -RPP38 14064 -RPP40 14065 -RPRD1A 14066 -RPRD1B 14067 -RPRD2 14068 -RPRM 14069 -RPRML 14070 -RPS10 14071 -RPS11 14072 -RPS12 14073 -RPS13 14074 -RPS14 14075 -RPS15 14076 -RPS15A 14077 -RPS16 14078 -RPS17 14079 -RPS18 14080 -RPS19 14081 -RPS19BP1 14082 -RPS2 14083 -RPS20 14084 -RPS21 14085 -RPS23 14086 -RPS24 14087 -RPS25 14088 -RPS26 14089 -RPS27 14090 -RPS27A 14091 -RPS27L 14092 -RPS28 14093 -RPS29 14094 -RPS3 14095 -RPS3A 14096 -RPS4X 14097 -RPS4Y1 14098 -RPS4Y2 14099 -RPS5 14100 -RPS6 14101 -RPS6KA1 14102 -RPS6KA2 14103 -RPS6KA3 14104 -RPS6KA4 14105 -RPS6KA5 14106 -RPS6KA6 14107 -RPS6KB1 14108 -RPS6KB2 14109 -RPS6KC1 14110 -RPS6KL1 14111 -RPS7 14112 -RPS8 14113 -RPS9 14114 -RPSA 14115 -RPTN 14116 -RPTOR 14117 -RPUSD1 14118 -RPUSD2 14119 -RPUSD3 14120 -RPUSD4 14121 -RRAD 14122 -RRAGA 14123 -RRAGB 14124 -RRAGC 14125 -RRAGD 14126 -RRAS 14127 -RRAS2 14128 -RRBP1 14129 -RREB1 14130 -RRH 14131 -RRM1 14132 -RRM2 14133 -RRM2B 14134 -RRN3 14135 -RRP1 14136 -RRP12 14137 -RRP15 14138 -RRP1B 14139 -RRP36 14140 -RRP7A 14141 -RRP8 14142 -RRP9 14143 -RRS1 14144 -RS1 14145 -RSAD1 14146 -RSAD2 14147 -RSBN1 14148 -RSBN1L 14149 -RSC1A1 14150 -RSF1 14151 -RSKR 14152 -RSL1D1 14153 -RSL24D1 14154 -RSPH1 14155 -RSPH10B 14156 -RSPH10B2 14157 -RSPH14 14158 -RSPH3 14159 -RSPH4A 14160 -RSPH6A 14161 -RSPH9 14162 -RSPO1 14163 -RSPO2 14164 -RSPO3 14165 -RSPO4 14166 -RSPRY1 14167 -RSRC1 14168 -RSRC2 14169 -RSRP1 14170 -RSU1 14171 -RTBDN 14172 -RTCA 14173 -RTCB 14174 -RTEL1 14175 -RTF1 14176 -RTF2 14177 -RTKN 14178 -RTKN2 14179 -RTL1 14180 -RTL10 14181 -RTL3 14182 -RTL4 14183 -RTL5 14184 -RTL6 14185 -RTL8A 14186 -RTL8B 14187 -RTL8C 14188 -RTL9 14189 -RTN1 14190 -RTN2 14191 -RTN3 14192 -RTN4 14193 -RTN4IP1 14194 -RTN4R 14195 -RTN4RL1 14196 -RTN4RL2 14197 -RTP1 14198 -RTP2 14199 -RTP3 14200 -RTP4 14201 -RTP5 14202 -RTRAF 14203 -RTTN 14204 -RUBCN 14205 -RUBCNL 14206 -RUFY1 14207 -RUFY2 14208 -RUFY3 14209 -RUFY4 14210 -RUNDC1 14211 -RUNDC3A 14212 -RUNDC3B 14213 -RUNX1 14214 -RUNX1T1 14215 -RUNX2 14216 -RUNX3 14217 -RUSC1 14218 -RUSC2 14219 -RUSF1 14220 -RUVBL1 14221 -RUVBL2 14222 -RWDD1 14223 -RWDD2A 14224 -RWDD2B 14225 -RWDD3 14226 -RWDD4 14227 -RXFP1 14228 -RXFP2 14229 -RXFP3 14230 -RXFP4 14231 -RXRA 14232 -RXRB 14233 -RXRG 14234 -RXYLT1 14235 -RYBP 14236 -RYK 14237 -RYR1 14238 -RYR2 14239 -RYR3 14240 -S100A1 14241 -S100A10 14242 -S100A11 14243 -S100A12 14244 -S100A13 14245 -S100A14 14246 -S100A16 14247 -S100A2 14248 -S100A3 14249 -S100A4 14250 -S100A5 14251 -S100A6 14252 -S100A7 14253 -S100A7A 14254 -S100A8 14255 -S100A9 14256 -S100B 14257 -S100G 14258 -S100P 14259 -S100PBP 14260 -S100Z 14261 -S1PR1 14262 -S1PR2 14263 -S1PR3 14264 -S1PR4 14265 -S1PR5 14266 -SAA1 14267 -SAA2 14268 -SAA4 14269 -SAAL1 14270 -SAC3D1 14271 -SACM1L 14272 -SACS 14273 -SAE1 14274 -SAFB 14275 -SAFB2 14276 -SAG 14277 -SAGE1 14278 -SALL1 14279 -SALL2 14280 -SALL3 14281 -SALL4 14282 -SAMD1 14283 -SAMD10 14284 -SAMD11 14285 -SAMD12 14286 -SAMD13 14287 -SAMD14 14288 -SAMD15 14289 -SAMD3 14290 -SAMD4A 14291 -SAMD4B 14292 -SAMD5 14293 -SAMD7 14294 -SAMD8 14295 -SAMD9 14296 -SAMD9L 14297 -SAMHD1 14298 -SAMM50 14299 -SAMSN1 14300 -SANBR 14301 -SAP130 14302 -SAP18 14303 -SAP25 14304 -SAP30 14305 -SAP30BP 14306 -SAP30L 14307 -SAPCD1 14308 -SAPCD2 14309 -SAR1A 14310 -SAR1B 14311 -SARAF 14312 -SARDH 14313 -SARM1 14314 -SARNP 14315 -SARS1 14316 -SARS2 14317 -SART1 14318 -SART3 14319 -SASH1 14320 -SASH3 14321 -SASS6 14322 -SAT1 14323 -SAT2 14324 -SATB1 14325 -SATB2 14326 -SATL1 14327 -SAV1 14328 -SAXO1 14329 -SAXO2 14330 -SAYSD1 14331 -SBDS 14332 -SBF1 14333 -SBF2 14334 -SBK1 14335 -SBK2 14336 -SBK3 14337 -SBNO1 14338 -SBNO2 14339 -SBSN 14340 -SBSPON 14341 -SC5D 14342 -SCAF1 14343 -SCAF11 14344 -SCAF4 14345 -SCAF8 14346 -SCAI 14347 -SCAMP1 14348 -SCAMP2 14349 -SCAMP3 14350 -SCAMP4 14351 -SCAMP5 14352 -SCAND1 14353 -SCAP 14354 -SCAPER 14355 -SCARA3 14356 -SCARA5 14357 -SCARB1 14358 -SCARB2 14359 -SCARF1 14360 -SCARF2 14361 -SCART1 14362 -SCCPDH 14363 -SCD 14364 -SCD5 14365 -SCEL 14366 -SCFD1 14367 -SCFD2 14368 -SCG2 14369 -SCG3 14370 -SCG5 14371 -SCGB1A1 14372 -SCGB1C1 14373 -SCGB1C2 14374 -SCGB1D1 14375 -SCGB1D2 14376 -SCGB1D4 14377 -SCGB2A1 14378 -SCGB2A2 14379 -SCGB2B2 14380 -SCGB3A1 14381 -SCGB3A2 14382 -SCGN 14383 -SCHIP1 14384 -SCIMP 14385 -SCIN 14386 -SCLT1 14387 -SCLY 14388 -SCMH1 14389 -SCML1 14390 -SCML2 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-SMIM39 15374 -SMIM4 15375 -SMIM40 15376 -SMIM41 15377 -SMIM42 15378 -SMIM43 15379 -SMIM44 15380 -SMIM45 15381 -SMIM5 15382 -SMIM6 15383 -SMIM7 15384 -SMIM8 15385 -SMIM9 15386 -SMKR1 15387 -SMLR1 15388 -SMN1 15389 -SMN2 15390 -SMNDC1 15391 -SMO 15392 -SMOC1 15393 -SMOC2 15394 -SMOX 15395 -SMPD1 15396 -SMPD2 15397 -SMPD3 15398 -SMPD4 15399 -SMPDL3A 15400 -SMPDL3B 15401 -SMPX 15402 -SMR3A 15403 -SMR3B 15404 -SMS 15405 -SMTN 15406 -SMTNL1 15407 -SMTNL2 15408 -SMU1 15409 -SMUG1 15410 -SMURF1 15411 -SMURF2 15412 -SMYD1 15413 -SMYD2 15414 -SMYD3 15415 -SMYD4 15416 -SMYD5 15417 -SNAI1 15418 -SNAI2 15419 -SNAI3 15420 -SNAP23 15421 -SNAP25 15422 -SNAP29 15423 -SNAP47 15424 -SNAP91 15425 -SNAPC1 15426 -SNAPC2 15427 -SNAPC3 15428 -SNAPC4 15429 -SNAPC5 15430 -SNAPIN 15431 -SNCA 15432 -SNCAIP 15433 -SNCB 15434 -SNCG 15435 -SND1 15436 -SNED1 15437 -SNF8 15438 -SNIP1 15439 -SNN 15440 -SNORC 15441 -SNPH 15442 -SNRK 15443 -SNRNP200 15444 -SNRNP25 15445 -SNRNP27 15446 -SNRNP35 15447 -SNRNP40 15448 -SNRNP48 15449 -SNRNP70 15450 -SNRPA 15451 -SNRPA1 15452 -SNRPB 15453 -SNRPB2 15454 -SNRPC 15455 -SNRPD1 15456 -SNRPD2 15457 -SNRPD3 15458 -SNRPE 15459 -SNRPF 15460 -SNRPG 15461 -SNRPN 15462 -SNTA1 15463 -SNTB1 15464 -SNTB2 15465 -SNTG1 15466 -SNTG2 15467 -SNTN 15468 -SNU13 15469 -SNUPN 15470 -SNURF 15471 -SNW1 15472 -SNX1 15473 -SNX10 15474 -SNX11 15475 -SNX12 15476 -SNX13 15477 -SNX14 15478 -SNX15 15479 -SNX16 15480 -SNX17 15481 -SNX18 15482 -SNX19 15483 -SNX2 15484 -SNX20 15485 -SNX21 15486 -SNX22 15487 -SNX24 15488 -SNX25 15489 -SNX27 15490 -SNX29 15491 -SNX3 15492 -SNX30 15493 -SNX31 15494 -SNX32 15495 -SNX33 15496 -SNX4 15497 -SNX5 15498 -SNX6 15499 -SNX7 15500 -SNX8 15501 -SNX9 15502 -SOAT1 15503 -SOAT2 15504 -SOBP 15505 -SOCS1 15506 -SOCS2 15507 -SOCS3 15508 -SOCS4 15509 -SOCS5 15510 -SOCS6 15511 -SOCS7 15512 -SOD1 15513 -SOD2 15514 -SOD3 15515 -SOGA1 15516 -SOGA3 15517 -SOHLH1 15518 -SOHLH2 15519 -SON 15520 -SORBS1 15521 -SORBS2 15522 -SORBS3 15523 -SORCS1 15524 -SORCS2 15525 -SORCS3 15526 -SORD 15527 -SORL1 15528 -SORT1 15529 -SOST 15530 -SOSTDC1 15531 -SOS1 15532 -SOS2 15533 -SOWAHA 15534 -SOWAHB 15535 -SOWAHC 15536 -SOWAHD 15537 -SOX1 15538 -SOX10 15539 -SOX11 15540 -SOX12 15541 -SOX13 15542 -SOX14 15543 -SOX15 15544 -SOX17 15545 -SOX18 15546 -SOX2 15547 -SOX21 15548 -SOX3 15549 -SOX30 15550 -SOX4 15551 -SOX5 15552 -SOX6 15553 -SOX7 15554 -SOX8 15555 -SOX9 15556 -SP1 15557 -SP100 15558 -SP110 15559 -SP140 15560 -SP140L 15561 -SP2 15562 -SP3 15563 -SP4 15564 -SP5 15565 -SP6 15566 -SP7 15567 -SP8 15568 -SP9 15569 -SPA17 15570 -SPAAR 15571 -SPACA1 15572 -SPACA3 15573 -SPACA4 15574 -SPACA5 15575 -SPACA5B 15576 -SPACA6 15577 -SPACA7 15578 -SPACA9 15579 -SPACDR 15580 -SPAG1 15581 -SPAG11A 15582 -SPAG11B 15583 -SPAG16 15584 -SPAG17 15585 -SPAG4 15586 -SPAG5 15587 -SPAG6 15588 -SPAG7 15589 -SPAG8 15590 -SPAG9 15591 -SPAM1 15592 -SPANXA1 15593 -SPANXA2 15594 -SPANXB1 15595 -SPANXC 15596 -SPANXD 15597 -SPANXN1 15598 -SPANXN2 15599 -SPANXN3 15600 -SPANXN4 15601 -SPANXN5 15602 -SPARC 15603 -SPARCL1 15604 -SPART 15605 -SPAST 15606 -SPATA1 15607 -SPATA12 15608 -SPATA13 15609 -SPATA16 15610 -SPATA17 15611 -SPATA18 15612 -SPATA19 15613 -SPATA2 15614 -SPATA20 15615 -SPATA21 15616 -SPATA22 15617 -SPATA24 15618 -SPATA25 15619 -SPATA2L 15620 -SPATA3 15621 -SPATA31A1 15622 -SPATA31A3 15623 -SPATA31A5 15624 -SPATA31A6 15625 -SPATA31A7 15626 -SPATA31D1 15627 -SPATA31D3 15628 -SPATA31D4 15629 -SPATA31E1 15630 -SPATA32 15631 -SPATA33 15632 -SPATA4 15633 -SPATA45 15634 -SPATA46 15635 -SPATA48 15636 -SPATA5 15637 -SPATA5L1 15638 -SPATA6 15639 -SPATA6L 15640 -SPATA7 15641 -SPATA9 15642 -SPATC1 15643 -SPATC1L 15644 -SPATS1 15645 -SPATS2 15646 -SPATS2L 15647 -SPC24 15648 -SPC25 15649 -SPCS1 15650 -SPCS2 15651 -SPCS3 15652 -SPDEF 15653 -SPDL1 15654 -SPDYA 15655 -SPDYC 15656 -SPDYE1 15657 -SPDYE10 15658 -SPDYE11 15659 -SPDYE12 15660 -SPDYE13 15661 -SPDYE14 15662 -SPDYE15 15663 -SPDYE16 15664 -SPDYE17 15665 -SPDYE18 15666 -SPDYE2 15667 -SPDYE21 15668 -SPDYE2B 15669 -SPDYE3 15670 -SPDYE4 15671 -SPDYE5 15672 -SPDYE6 15673 -SPDYE8 15674 -SPDYE9 15675 -SPECC1 15676 -SPECC1L 15677 -SPEF1 15678 -SPEF2 15679 -SPEG 15680 -SPEGNB 15681 -SPEM1 15682 -SPEM2 15683 -SPEM3 15684 -SPEN 15685 -SPESP1 15686 -SPG11 15687 -SPG21 15688 -SPG7 15689 -SPHK1 15690 -SPHK2 15691 -SPHKAP 15692 -SPI1 15693 -SPIB 15694 -SPIC 15695 -SPICE1 15696 -SPIDR 15697 -SPIN1 15698 -SPIN2A 15699 -SPIN2B 15700 -SPIN3 15701 -SPIN4 15702 -SPINDOC 15703 -SPINK1 15704 -SPINK13 15705 -SPINK14 15706 -SPINK2 15707 -SPINK4 15708 -SPINK5 15709 -SPINK6 15710 -SPINK7 15711 -SPINK8 15712 -SPINK9 15713 -SPINT1 15714 -SPINT2 15715 -SPINT3 15716 -SPINT4 15717 -SPIRE1 15718 -SPIRE2 15719 -SPN 15720 -SPNS1 15721 -SPNS2 15722 -SPNS3 15723 -SPO11 15724 -SPOCD1 15725 -SPOCK1 15726 -SPOCK2 15727 -SPOCK3 15728 -SPON1 15729 -SPON2 15730 -SPOP 15731 -SPOPL 15732 -SPOUT1 15733 -SPP1 15734 -SPP2 15735 -SPPL2A 15736 -SPPL2B 15737 -SPPL2C 15738 -SPPL3 15739 -SPR 15740 -SPRED1 15741 -SPRED2 15742 -SPRED3 15743 -SPRING1 15744 -SPRN 15745 -SPRR1A 15746 -SPRR1B 15747 -SPRR2A 15748 -SPRR2B 15749 -SPRR2D 15750 -SPRR2E 15751 -SPRR2F 15752 -SPRR2G 15753 -SPRR3 15754 -SPRR4 15755 -SPRR5 15756 -SPRTN 15757 -SPRY1 15758 -SPRY2 15759 -SPRY3 15760 -SPRY4 15761 -SPRYD3 15762 -SPRYD4 15763 -SPRYD7 15764 -SPSB1 15765 -SPSB2 15766 -SPSB3 15767 -SPSB4 15768 -SPTA1 15769 -SPTAN1 15770 -SPTB 15771 -SPTBN1 15772 -SPTBN2 15773 -SPTBN4 15774 -SPTBN5 15775 -SPTLC1 15776 -SPTLC2 15777 -SPTLC3 15778 -SPTSSA 15779 -SPTSSB 15780 -SPTY2D1 15781 -SPTY2D1OS 15782 -SPX 15783 -SPZ1 15784 -SQLE 15785 -SQOR 15786 -SQSTM1 15787 -SRA1 15788 -SRARP 15789 -SRBD1 15790 -SRC 15791 -SRCAP 15792 -SRCIN1 15793 -SRD5A1 15794 -SRD5A2 15795 -SRD5A3 15796 -SREBF1 15797 -SREBF2 15798 -SREK1 15799 -SREK1IP1 15800 -SRF 15801 -SRFBP1 15802 -SRGAP1 15803 -SRGAP2 15804 -SRGAP2B 15805 -SRGAP2C 15806 -SRGAP3 15807 -SRGN 15808 -SRI 15809 -SRL 15810 -SRM 15811 -SRMS 15812 -SRP14 15813 -SRP19 15814 -SRP54 15815 -SRP68 15816 -SRP72 15817 -SRP9 15818 -SRPK1 15819 -SRPK2 15820 -SRPK3 15821 -SRPRA 15822 -SRPRB 15823 -SRPX 15824 -SRPX2 15825 -SRR 15826 -SRRD 15827 -SRRM1 15828 -SRRM2 15829 -SRRM3 15830 -SRRM4 15831 -SRRM5 15832 -SRRT 15833 -SRSF1 15834 -SRSF10 15835 -SRSF11 15836 -SRSF12 15837 -SRSF2 15838 -SRSF3 15839 -SRSF4 15840 -SRSF5 15841 -SRSF6 15842 -SRSF7 15843 -SRSF8 15844 -SRSF9 15845 -SRXN1 15846 -SRY 15847 -SS18 15848 -SS18L1 15849 -SS18L2 15850 -SSB 15851 -SSBP1 15852 -SSBP2 15853 -SSBP3 15854 -SSBP4 15855 -SSC4D 15856 -SSC5D 15857 -SSH1 15858 -SSH2 15859 -SSH3 15860 -SSMEM1 15861 -SSNA1 15862 -SSPN 15863 -SSR1 15864 -SSR2 15865 -SSR3 15866 -SSR4 15867 -SSRP1 15868 -SST 15869 -SSTR1 15870 -SSTR2 15871 -SSTR3 15872 -SSTR4 15873 -SSTR5 15874 -SSU72 15875 -SSUH2 15876 -SSX1 15877 -SSX2 15878 -SSX2B 15879 -SSX2IP 15880 -SSX3 15881 -SSX4 15882 -SSX4B 15883 -SSX5 15884 -SSX7 15885 -ST13 15886 -ST14 15887 -ST18 15888 -ST20 15889 -ST3GAL1 15890 -ST3GAL2 15891 -ST3GAL3 15892 -ST3GAL4 15893 -ST3GAL5 15894 -ST3GAL6 15895 -ST6GAL1 15896 -ST6GAL2 15897 -ST6GALNAC1 15898 -ST6GALNAC2 15899 -ST6GALNAC3 15900 -ST6GALNAC4 15901 -ST6GALNAC5 15902 -ST6GALNAC6 15903 -ST7 15904 -ST7L 15905 -ST8SIA1 15906 -ST8SIA2 15907 -ST8SIA3 15908 -ST8SIA4 15909 -ST8SIA5 15910 -ST8SIA6 15911 -STAB1 15912 -STAB2 15913 -STAC 15914 -STAC2 15915 -STAC3 15916 -STAG1 15917 -STAG2 15918 -STAG3 15919 -STAM 15920 -STAM2 15921 -STAMBP 15922 -STAMBPL1 15923 -STAP1 15924 -STAP2 15925 -STAR 15926 -STARD10 15927 -STARD13 15928 -STARD3 15929 -STARD3NL 15930 -STARD4 15931 -STARD5 15932 -STARD6 15933 -STARD7 15934 -STARD8 15935 -STARD9 15936 -STAT1 15937 -STAT2 15938 -STAT3 15939 -STAT4 15940 -STAT5A 15941 -STAT5B 15942 -STAT6 15943 -STATH 15944 -STAU1 15945 -STAU2 15946 -STBD1 15947 -STC1 15948 -STC2 15949 -STEAP1 15950 -STEAP1B 15951 -STEAP2 15952 -STEAP3 15953 -STEAP4 15954 -STEEP1 15955 -STH 15956 -STIL 15957 -STIM1 15958 -STIM2 15959 -STIMATE 15960 -STING1 15961 -STIP1 15962 -STK10 15963 -STK11 15964 -STK11IP 15965 -STK16 15966 -STK17A 15967 -STK17B 15968 -STK19 15969 -STK24 15970 -STK25 15971 -STK26 15972 -STK3 15973 -STK31 15974 -STK32A 15975 -STK32B 15976 -STK32C 15977 -STK33 15978 -STK35 15979 -STK36 15980 -STK38 15981 -STK38L 15982 -STK39 15983 -STK4 15984 -STK40 15985 -STKLD1 15986 -STMN1 15987 -STMN2 15988 -STMN3 15989 -STMN4 15990 -STMND1 15991 -STMP1 15992 -STN1 15993 -STOM 15994 -STOML1 15995 -STOML2 15996 -STOML3 15997 -STON1 15998 -STON2 15999 -STOX1 16000 -STOX2 16001 -STPG1 16002 -STPG2 16003 -STPG3 16004 -STPG4 16005 -STRA6 16006 -STRA8 16007 -STRADA 16008 -STRADB 16009 -STRAP 16010 -STRBP 16011 -STRC 16012 -STRIP1 16013 -STRIP2 16014 -STRIT1 16015 -STRN 16016 -STRN3 16017 -STRN4 16018 -STS 16019 -STT3A 16020 -STT3B 16021 -STUB1 16022 -STUM 16023 -STX10 16024 -STX11 16025 -STX12 16026 -STX16 16027 -STX17 16028 -STX18 16029 -STX19 16030 -STX1A 16031 -STX1B 16032 -STX2 16033 -STX3 16034 -STX4 16035 -STX5 16036 -STX6 16037 -STX7 16038 -STX8 16039 -STXBP1 16040 -STXBP2 16041 -STXBP3 16042 -STXBP4 16043 -STXBP5 16044 -STXBP5L 16045 -STXBP6 16046 -STYK1 16047 -STYX 16048 -STYXL1 16049 -STYXL2 16050 -SUB1 16051 -SUCLA2 16052 -SUCLG1 16053 -SUCLG2 16054 -SUCNR1 16055 -SUCO 16056 -SUDS3 16057 -SUFU 16058 -SUGCT 16059 -SUGP1 16060 -SUGP2 16061 -SUGT1 16062 -SULF1 16063 -SULF2 16064 -SULT1A1 16065 -SULT1A2 16066 -SULT1A3 16067 -SULT1A4 16068 -SULT1B1 16069 -SULT1C2 16070 -SULT1C3 16071 -SULT1C4 16072 -SULT1E1 16073 -SULT2A1 16074 -SULT2B1 16075 -SULT4A1 16076 -SULT6B1 16077 -SUMF1 16078 -SUMF2 16079 -SUMO1 16080 -SUMO2 16081 -SUMO3 16082 -SUMO4 16083 -SUN1 16084 -SUN2 16085 -SUN3 16086 -SUN5 16087 -SUOX 16088 -SUPT16H 16089 -SUPT20H 16090 -SUPT20HL1 16091 -SUPT20HL2 16092 -SUPT3H 16093 -SUPT4H1 16094 -SUPT5H 16095 -SUPT6H 16096 -SUPT7L 16097 -SUPV3L1 16098 -SURF1 16099 -SURF2 16100 -SURF4 16101 -SURF6 16102 -SUSD1 16103 -SUSD2 16104 -SUSD3 16105 -SUSD4 16106 -SUSD5 16107 -SUSD6 16108 -SUV39H1 16109 -SUV39H2 16110 -SUZ12 16111 -SV2A 16112 -SV2B 16113 -SV2C 16114 -SVBP 16115 -SVEP1 16116 -SVIL 16117 -SVIP 16118 -SVOP 16119 -SVOPL 16120 -SWAP70 16121 -SWI5 16122 -SWSAP1 16123 -SWT1 16124 -SYAP1 16125 -SYBU 16126 -SYCE1 16127 -SYCE1L 16128 -SYCE2 16129 -SYCE3 16130 -SYCN 16131 -SYCP1 16132 -SYCP2 16133 -SYCP2L 16134 -SYCP3 16135 -SYDE1 16136 -SYDE2 16137 -SYF2 16138 -SYK 16139 -SYMPK 16140 -SYN1 16141 -SYN2 16142 -SYN3 16143 -SYNC 16144 -SYNCRIP 16145 -SYNDIG1 16146 -SYNDIG1L 16147 -SYNE1 16148 -SYNE2 16149 -SYNE3 16150 -SYNE4 16151 -SYNGAP1 16152 -SYNGR1 16153 -SYNGR2 16154 -SYNGR3 16155 -SYNGR4 16156 -SYNJ1 16157 -SYNJ2 16158 -SYNJ2BP 16159 -SYNM 16160 -SYNPO 16161 -SYNPO2 16162 -SYNPO2L 16163 -SYNPR 16164 -SYNRG 16165 -SYP 16166 -SYPL1 16167 -SYPL2 16168 -SYS1 16169 -SYT1 16170 -SYT10 16171 -SYT11 16172 -SYT12 16173 -SYT13 16174 -SYT14 16175 -SYT15 16176 -SYT15B 16177 -SYT16 16178 -SYT17 16179 -SYT2 16180 -SYT3 16181 -SYT4 16182 -SYT5 16183 -SYT6 16184 -SYT7 16185 -SYT8 16186 -SYT9 16187 -SYTL1 16188 -SYTL2 16189 -SYTL3 16190 -SYTL4 16191 -SYTL5 16192 -SYVN1 16193 -SZRD1 16194 -SZT2 16195 -TAAR1 16196 -TAAR2 16197 -TAAR5 16198 -TAAR6 16199 -TAAR8 16200 -TAAR9 16201 -TAB1 16202 -TAB2 16203 -TAB3 16204 -TAC1 16205 -TAC3 16206 -TAC4 16207 -TACC1 16208 -TACC2 16209 -TACC3 16210 -TACO1 16211 -TACR1 16212 -TACR2 16213 -TACR3 16214 -TACSTD2 16215 -TADA1 16216 -TADA2A 16217 -TADA2B 16218 -TADA3 16219 -TAF1 16220 -TAF10 16221 -TAF11 16222 -TAF11L10 16223 -TAF11L11 16224 -TAF11L12 16225 -TAF11L13 16226 -TAF11L14 16227 -TAF11L2 16228 -TAF11L3 16229 -TAF11L4 16230 -TAF11L5 16231 -TAF11L6 16232 -TAF11L7 16233 -TAF11L8 16234 -TAF11L9 16235 -TAF12 16236 -TAF13 16237 -TAF15 16238 -TAF1A 16239 -TAF1B 16240 -TAF1C 16241 -TAF1D 16242 -TAF1L 16243 -TAF2 16244 -TAF3 16245 -TAF4 16246 -TAF4B 16247 -TAF5 16248 -TAF5L 16249 -TAF6 16250 -TAF6L 16251 -TAF7 16252 -TAF7L 16253 -TAF8 16254 -TAF9 16255 -TAF9B 16256 -TAFA1 16257 -TAFA2 16258 -TAFA3 16259 -TAFA4 16260 -TAFA5 16261 -TAFAZZIN 16262 -TAGAP 16263 -TAGLN 16264 -TAGLN2 16265 -TAGLN3 16266 -TAL1 16267 -TAL2 16268 -TALDO1 16269 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16340 -TBC1D17 16341 -TBC1D19 16342 -TBC1D2 16343 -TBC1D20 16344 -TBC1D21 16345 -TBC1D22A 16346 -TBC1D22B 16347 -TBC1D23 16348 -TBC1D24 16349 -TBC1D25 16350 -TBC1D26 16351 -TBC1D28 16352 -TBC1D2B 16353 -TBC1D3 16354 -TBC1D30 16355 -TBC1D31 16356 -TBC1D32 16357 -TBC1D3B 16358 -TBC1D3C 16359 -TBC1D3D 16360 -TBC1D3E 16361 -TBC1D3F 16362 -TBC1D3G 16363 -TBC1D3H 16364 -TBC1D3I 16365 -TBC1D3K 16366 -TBC1D3L 16367 -TBC1D4 16368 -TBC1D5 16369 -TBC1D7 16370 -TBC1D8 16371 -TBC1D8B 16372 -TBC1D9 16373 -TBC1D9B 16374 -TBCA 16375 -TBCB 16376 -TBCC 16377 -TBCCD1 16378 -TBCD 16379 -TBCE 16380 -TBCEL 16381 -TBCK 16382 -TBK1 16383 -TBKBP1 16384 -TBL1X 16385 -TBL1XR1 16386 -TBL1Y 16387 -TBL2 16388 -TBL3 16389 -TBP 16390 -TBPL1 16391 -TBPL2 16392 -TBR1 16393 -TBRG1 16394 -TBRG4 16395 -TBX1 16396 -TBX10 16397 -TBX15 16398 -TBX18 16399 -TBX19 16400 -TBX2 16401 -TBX20 16402 -TBX21 16403 -TBX22 16404 -TBX3 16405 -TBX4 16406 -TBX5 16407 -TBX6 16408 -TBXA2R 16409 -TBXAS1 16410 -TBXT 16411 -TC2N 16412 -TCAF1 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-TSC22D2 17501 -TSC22D3 17502 -TSC22D4 17503 -TSEN15 17504 -TSEN2 17505 -TSEN34 17506 -TSEN54 17507 -TSFM 17508 -TSG101 17509 -TSGA10 17510 -TSGA10IP 17511 -TSGA13 17512 -TSHB 17513 -TSHR 17514 -TSHZ1 17515 -TSHZ2 17516 -TSHZ3 17517 -TSKS 17518 -TSKU 17519 -TSLP 17520 -TSN 17521 -TSNARE1 17522 -TSNAX 17523 -TSNAXIP1 17524 -TSPAN1 17525 -TSPAN10 17526 -TSPAN11 17527 -TSPAN12 17528 -TSPAN13 17529 -TSPAN14 17530 -TSPAN15 17531 -TSPAN16 17532 -TSPAN17 17533 -TSPAN18 17534 -TSPAN19 17535 -TSPAN2 17536 -TSPAN3 17537 -TSPAN31 17538 -TSPAN32 17539 -TSPAN33 17540 -TSPAN4 17541 -TSPAN5 17542 -TSPAN6 17543 -TSPAN7 17544 -TSPAN8 17545 -TSPAN9 17546 -TSPEAR 17547 -TSPO 17548 -TSPO2 17549 -TSPOAP1 17550 -TSPY1 17551 -TSPY10 17552 -TSPY2 17553 -TSPY3 17554 -TSPY4 17555 -TSPY8 17556 -TSPY9 17557 -TSPYL1 17558 -TSPYL2 17559 -TSPYL4 17560 -TSPYL5 17561 -TSPYL6 17562 -TSR1 17563 -TSR2 17564 -TSR3 17565 -TSSC4 17566 -TSSK1B 17567 -TSSK2 17568 -TSSK3 17569 -TSSK4 17570 -TSSK6 17571 -TST 17572 -TSTD1 17573 -TSTD2 17574 -TSTD3 17575 -TTBK1 17576 -TTBK2 17577 -TTC1 17578 -TTC12 17579 -TTC13 17580 -TTC14 17581 -TTC16 17582 -TTC17 17583 -TTC19 17584 -TTC21A 17585 -TTC21B 17586 -TTC22 17587 -TTC23 17588 -TTC23L 17589 -TTC24 17590 -TTC26 17591 -TTC27 17592 -TTC28 17593 -TTC29 17594 -TTC3 17595 -TTC30A 17596 -TTC30B 17597 -TTC31 17598 -TTC32 17599 -TTC33 17600 -TTC34 17601 -TTC36 17602 -TTC37 17603 -TTC38 17604 -TTC39A 17605 -TTC39B 17606 -TTC39C 17607 -TTC4 17608 -TTC5 17609 -TTC6 17610 -TTC7A 17611 -TTC7B 17612 -TTC8 17613 -TTC9 17614 -TTC9B 17615 -TTC9C 17616 -TTF1 17617 -TTF2 17618 -TTI1 17619 -TTI2 17620 -TTK 17621 -TTL 17622 -TTLL1 17623 -TTLL10 17624 -TTLL11 17625 -TTLL12 17626 -TTLL13 17627 -TTLL2 17628 -TTLL3 17629 -TTLL4 17630 -TTLL5 17631 -TTLL6 17632 -TTLL7 17633 -TTLL8 17634 -TTLL9 17635 -TTN 17636 -TTPA 17637 -TTPAL 17638 -TTR 17639 -TTYH1 17640 -TTYH2 17641 -TTYH3 17642 -TUB 17643 -TUBA1A 17644 -TUBA1B 17645 -TUBA1C 17646 -TUBA3C 17647 -TUBA3D 17648 -TUBA3E 17649 -TUBA4A 17650 -TUBA4B 17651 -TUBA8 17652 -TUBAL3 17653 -TUBB 17654 -TUBB1 17655 -TUBB2A 17656 -TUBB2B 17657 -TUBB3 17658 -TUBB4A 17659 -TUBB4B 17660 -TUBB6 17661 -TUBB8 17662 -TUBB8B 17663 -TUBD1 17664 -TUBE1 17665 -TUBG1 17666 -TUBG2 17667 -TUBGCP2 17668 -TUBGCP3 17669 -TUBGCP4 17670 -TUBGCP5 17671 -TUBGCP6 17672 -TUFM 17673 -TUFT1 17674 -TUG1 17675 -TULP1 17676 -TULP2 17677 -TULP3 17678 -TULP4 17679 -TUSC1 17680 -TUSC2 17681 -TUSC3 17682 -TUT1 17683 -TUT4 17684 -TUT7 17685 -TVP23A 17686 -TVP23B 17687 -TVP23C 17688 -TWF1 17689 -TWF2 17690 -TWIST1 17691 -TWIST2 17692 -TWNK 17693 -TWSG1 17694 -TXK 17695 -TXLNA 17696 -TXLNB 17697 -TXLNG 17698 -TXN 17699 -TXN2 17700 -TXNDC11 17701 -TXNDC12 17702 -TXNDC15 17703 -TXNDC16 17704 -TXNDC17 17705 -TXNDC2 17706 -TXNDC5 17707 -TXNDC8 17708 -TXNDC9 17709 -TXNIP 17710 -TXNL1 17711 -TXNL4A 17712 -TXNL4B 17713 -TXNRD1 17714 -TXNRD2 17715 -TXNRD3 17716 -TYK2 17717 -TYMP 17718 -TYMS 17719 -TYR 17720 -TYRO3 17721 -TYROBP 17722 -TYRP1 17723 -TYSND1 17724 -TYW1 17725 -TYW1B 17726 -TYW3 17727 -TYW5 17728 -U2AF1 17729 -U2AF1L4 17730 -U2AF2 17731 -U2SURP 17732 -UACA 17733 -UAP1 17734 -UAP1L1 17735 -UBA1 17736 -UBA2 17737 -UBA3 17738 -UBA5 17739 -UBA52 17740 -UBA6 17741 -UBA7 17742 -UBAC1 17743 -UBAC2 17744 -UBALD1 17745 -UBALD2 17746 -UBAP1 17747 -UBAP1L 17748 -UBAP2 17749 -UBAP2L 17750 -UBASH3A 17751 -UBASH3B 17752 -UBB 17753 -UBC 17754 -UBD 17755 -UBE2A 17756 -UBE2B 17757 -UBE2C 17758 -UBE2D1 17759 -UBE2D2 17760 -UBE2D3 17761 -UBE2D4 17762 -UBE2E1 17763 -UBE2E2 17764 -UBE2E3 17765 -UBE2F 17766 -UBE2G1 17767 -UBE2G2 17768 -UBE2H 17769 -UBE2I 17770 -UBE2J1 17771 -UBE2J2 17772 -UBE2K 17773 -UBE2L3 17774 -UBE2L5 17775 -UBE2L6 17776 -UBE2M 17777 -UBE2N 17778 -UBE2NL 17779 -UBE2O 17780 -UBE2Q1 17781 -UBE2Q2 17782 -UBE2Q2L 17783 -UBE2QL1 17784 -UBE2R2 17785 -UBE2S 17786 -UBE2T 17787 -UBE2U 17788 -UBE2V1 17789 -UBE2V2 17790 -UBE2W 17791 -UBE2Z 17792 -UBE3A 17793 -UBE3B 17794 -UBE3C 17795 -UBE3D 17796 -UBE4A 17797 -UBE4B 17798 -UBFD1 17799 -UBIAD1 17800 -UBL3 17801 -UBL4A 17802 -UBL4B 17803 -UBL5 17804 -UBL7 17805 -UBLCP1 17806 -UBN1 17807 -UBN2 17808 -UBOX5 17809 -UBP1 17810 -UBQLN1 17811 -UBQLN2 17812 -UBQLN3 17813 -UBQLN4 17814 -UBQLNL 17815 -UBR1 17816 -UBR2 17817 -UBR3 17818 -UBR4 17819 -UBR5 17820 -UBR7 17821 -UBTD1 17822 -UBTD2 17823 -UBTF 17824 -UBTFL1 17825 -UBXN1 17826 -UBXN10 17827 -UBXN11 17828 -UBXN2A 17829 -UBXN2B 17830 -UBXN4 17831 -UBXN6 17832 -UBXN7 17833 -UBXN8 17834 -UCHL1 17835 -UCHL3 17836 -UCHL5 17837 -UCK1 17838 -UCK2 17839 -UCKL1 17840 -UCMA 17841 -UCN 17842 -UCN2 17843 -UCN3 17844 -UCP1 17845 -UCP2 17846 -UCP3 17847 -UEVLD 17848 -UFC1 17849 -UFD1 17850 -UFL1 17851 -UFM1 17852 -UFSP1 17853 -UFSP2 17854 -UGCG 17855 -UGDH 17856 -UGGT1 17857 -UGGT2 17858 -UGP2 17859 -UGT1A 17860 -UGT2A1 17861 -UGT2A3 17862 -UGT2B10 17863 -UGT2B11 17864 -UGT2B15 17865 -UGT2B17 17866 -UGT2B28 17867 -UGT2B4 17868 -UGT2B7 17869 -UGT3A1 17870 -UGT3A2 17871 -UGT8 17872 -UHMK1 17873 -UHRF1 17874 -UHRF1BP1 17875 -UHRF1BP1L 17876 -UHRF2 17877 -UIMC1 17878 -ULBP1 17879 -ULBP2 17880 -ULBP3 17881 -ULK1 17882 -ULK2 17883 -ULK3 17884 -ULK4 17885 -UMAD1 17886 -UMOD 17887 -UMODL1 17888 -UMPS 17889 -UNC119 17890 -UNC119B 17891 -UNC13A 17892 -UNC13B 17893 -UNC13C 17894 -UNC13D 17895 -UNC45A 17896 -UNC45B 17897 -UNC50 17898 -UNC5A 17899 -UNC5B 17900 -UNC5C 17901 -UNC5CL 17902 -UNC5D 17903 -UNC79 17904 -UNC80 17905 -UNC93A 17906 -UNC93B1 17907 -UNCX 17908 -UNG 17909 -UNK 17910 -UNKL 17911 -UPB1 17912 -UPF1 17913 -UPF2 17914 -UPF3A 17915 -UPF3B 17916 -UPK1A 17917 -UPK1B 17918 -UPK2 17919 -UPK3A 17920 -UPK3B 17921 -UPK3BL1 17922 -UPK3BL2 17923 -UPP1 17924 -UPP2 17925 -UPRT 17926 -UQCC1 17927 -UQCC2 17928 -UQCC3 17929 -UQCR10 17930 -UQCR11 17931 -UQCRB 17932 -UQCRC1 17933 -UQCRC2 17934 -UQCRFS1 17935 -UQCRH 17936 -UQCRHL 17937 -UQCRQ 17938 -URAD 17939 -URB1 17940 -URB2 17941 -URGCP 17942 -URI1 17943 -URM1 17944 -UROC1 17945 -UROD 17946 -UROS 17947 -USB1 17948 -USE1 17949 -USF1 17950 -USF2 17951 -USF3 17952 -USH1C 17953 -USH1G 17954 -USH2A 17955 -USHBP1 17956 -USO1 17957 -USP1 17958 -USP10 17959 -USP11 17960 -USP12 17961 -USP13 17962 -USP14 17963 -USP15 17964 -USP16 17965 -USP17L1 17966 -USP17L10 17967 -USP17L11 17968 -USP17L12 17969 -USP17L13 17970 -USP17L15 17971 -USP17L17 17972 -USP17L18 17973 -USP17L19 17974 -USP17L2 17975 -USP17L20 17976 -USP17L21 17977 -USP17L22 17978 -USP17L23 17979 -USP17L24 17980 -USP17L25 17981 -USP17L26 17982 -USP17L27 17983 -USP17L28 17984 -USP17L29 17985 -USP17L3 17986 -USP17L30 17987 -USP17L4 17988 -USP17L5 17989 -USP17L7 17990 -USP17L8 17991 -USP18 17992 -USP19 17993 -USP2 17994 -USP20 17995 -USP21 17996 -USP22 17997 -USP24 17998 -USP25 17999 -USP26 18000 -USP27X 18001 -USP28 18002 -USP29 18003 -USP3 18004 -USP30 18005 -USP31 18006 -USP32 18007 -USP33 18008 -USP34 18009 -USP35 18010 -USP36 18011 -USP37 18012 -USP38 18013 -USP39 18014 -USP4 18015 -USP40 18016 -USP41 18017 -USP42 18018 -USP43 18019 -USP44 18020 -USP45 18021 -USP46 18022 -USP47 18023 -USP48 18024 -USP49 18025 -USP5 18026 -USP50 18027 -USP51 18028 -USP53 18029 -USP54 18030 -USP6 18031 -USP6NL 18032 -USP7 18033 -USP8 18034 -USP9X 18035 -USP9Y 18036 -USPL1 18037 -UST 18038 -UTF1 18039 -UTP11 18040 -UTP14A 18041 -UTP14C 18042 -UTP15 18043 -UTP18 18044 -UTP20 18045 -UTP23 18046 -UTP25 18047 -UTP3 18048 -UTP4 18049 -UTP6 18050 -UTRN 18051 -UTS2 18052 -UTS2B 18053 -UTS2R 18054 -UTY 18055 -UVRAG 18056 -UVSSA 18057 -UXS1 18058 -UXT 18059 -VAC14 18060 -VAMP1 18061 -VAMP2 18062 -VAMP3 18063 -VAMP4 18064 -VAMP5 18065 -VAMP7 18066 -VAMP8 18067 -VANGL1 18068 -VANGL2 18069 -VAPA 18070 -VAPB 18071 -VARS1 18072 -VARS2 18073 -VASH1 18074 -VASH2 18075 -VASN 18076 -VASP 18077 -VAT1 18078 -VAT1L 18079 -VAV1 18080 -VAV2 18081 -VAV3 18082 -VAX1 18083 -VAX2 18084 -VBP1 18085 -VCAM1 18086 -VCAN 18087 -VCL 18088 -VCP 18089 -VCPIP1 18090 -VCPKMT 18091 -VCX 18092 -VCX2 18093 -VCX3A 18094 -VCX3B 18095 -VCY 18096 -VCY1B 18097 -VDAC1 18098 -VDAC2 18099 -VDAC3 18100 -VDR 18101 -VEGFA 18102 -VEGFB 18103 -VEGFC 18104 -VEGFD 18105 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-import pandas as pd -import argparse -import torch -from tqdm import tqdm -import scipy.sparse -from scipy.sparse import issparse -import scanpy as sc -from load import * - - -def main_gene_selection(X_df, gene_list): - """ - Describe: - rebuild the input adata to select target genes encode protein - Parameters: - adata->`~anndata.AnnData` object: adata with var index_name by gene symbol - gene_list->list: wanted target gene - Returns: - adata_new->`~anndata.AnnData` object - to_fill_columns->list: zero padding gene - """ - to_fill_columns = list(set(gene_list) - set(X_df.columns)) - padding_df = pd.DataFrame(np.zeros((X_df.shape[0], len(to_fill_columns))), - columns=to_fill_columns, - index=X_df.index) - X_df = pd.DataFrame(np.concatenate([df.values for df in [X_df, padding_df]], axis=1), - index=X_df.index, - columns=list(X_df.columns) + list(padding_df.columns)) - X_df = X_df[gene_list] - - var = pd.DataFrame(index=X_df.columns) - var['mask'] = [1 if i in to_fill_columns else 0 for i in list(var.index)] - return X_df, to_fill_columns,var -gene_list_df = pd.read_csv('./OS_scRNA_gene_index.19264.tsv', header=0, delimiter='\t') -gene_list = list(gene_list_df['gene_name']) - -def main_get(task_name = 'bec', - input_type = 'singlecell', - output_type = 'cell', - pool_type = 'all', - tgthighres='t4', - data=None, - save_path='./', - pre_normalized='F', - demo='store_true', - version='ce', - model_path='None', - ckpt_name='01B-resolution'): - - args = argparse.Namespace() - args.task_name = task_name - args.input_type = input_type - args.output_type = output_type - args.pool_type = pool_type - args.tgthighres = tgthighres - args.data = data - args.save_path = save_path - args.pre_normalized = pre_normalized - args.demo = demo - args.version = version - args.model_path = model_path - args.ckpt_name = ckpt_name - - #Set random seed - random.seed(0) - np.random.seed(0) # numpy random generator - - torch.manual_seed(0) - torch.cuda.manual_seed_all(0) - - torch.backends.cudnn.deterministic = True - torch.backends.cudnn.benchmark = False - - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - #Load data - gexpr_feature = data - idx = gexpr_feature.obs_names.tolist() - try: - col = gexpr_feature.var.gene_name.tolist() - except: - col = gexpr_feature.var_names.tolist() - if issparse(gexpr_feature.X): - gexpr_feature = gexpr_feature.X.toarray() - else: - gexpr_feature = gexpr_feature.X - gexpr_feature = pd.DataFrame(gexpr_feature,index=idx,columns=col) - - if gexpr_feature.shape[1]<19264: - print('covert gene feature into 19264') - gexpr_feature, to_fill_columns,var = main_gene_selection(gexpr_feature,gene_list) - assert gexpr_feature.shape[1]>=19264 - - if (args.pre_normalized == 'F') and (args.input_type == 'bulk'): - adata = sc.AnnData(gexpr_feature) - sc.pp.normalize_total(adata) - sc.pp.log1p(adata) - gexpr_feature = pd.DataFrame(adata.X,index=adata.obs_names,columns=adata.var_names) - - if args.demo: - gexpr_feature = gexpr_feature.iloc[:10,:] - print(gexpr_feature.shape) - - #Load model - if args.version == 'noversion': - ckpt_path = args.model_path - key=None - else: - ckpt_path = './models/models.ckpt' - if args.output_type == 'cell': - if args.version == 'ce': - key = 'cell' - elif args.version == 'rde': - key = 'rde' - else: - raise ValueError('No version found') - elif args.output_type == 'gene': - key = 'gene' - elif args.output_type == 'gene_batch': - key = 'gene' - elif args.output_type == 'gene_expression': # Not recommended - key = 'gene' - else: - raise ValueError('output_mode must be one of cell gene, gene_batch, gene_expression') - pretrainmodel,pretrainconfig = load_model_frommmf(ckpt_path,key) - pretrainmodel.eval() - - geneexpemb=[] - batchcontainer = [] - strname = os.path.join(args.save_path, args.task_name +'_'+ args.ckpt_name +"_"+ args.input_type + '_' + args.output_type + '_embedding_' + args.tgthighres + '_resolution.npy') - print('save at {}'.format(strname)) - - #Inference - for i in tqdm(range(gexpr_feature.shape[0])): - with torch.no_grad(): - #Bulk - if args.input_type == 'bulk': - if args.pre_normalized == 'T': - totalcount = gexpr_feature.iloc[i,:].sum() - elif args.pre_normalized == 'F': - totalcount = np.log10(gexpr_feature.iloc[i,:].sum()) - else: - raise ValueError('pre_normalized must be T or F') - tmpdata = (gexpr_feature.iloc[i,:]).tolist() - pretrain_gene_x = torch.tensor(tmpdata+[totalcount,totalcount]).unsqueeze(0).cuda() - data_gene_ids = torch.arange(19266, device=pretrain_gene_x.device).repeat(pretrain_gene_x.shape[0], 1) - - #Single cell - elif args.input_type == 'singlecell': - # pre-Normalization - if args.pre_normalized == 'F': - tmpdata = (np.log1p(gexpr_feature.iloc[i,:]/(gexpr_feature.iloc[i,:].sum())*1e4)).tolist() - elif args.pre_normalized == 'T': - tmpdata = (gexpr_feature.iloc[i,:]).tolist() - elif args.pre_normalized == 'A': - tmpdata = (gexpr_feature.iloc[i,:-1]).tolist() - else: - raise ValueError('pre_normalized must be T,F or A') - - if args.pre_normalized == 'A': - totalcount = gexpr_feature.iloc[i,-1] - else: - totalcount = gexpr_feature.iloc[i,:].sum() - - # select resolution - if args.tgthighres[0] == 'f': - pretrain_gene_x = torch.tensor(tmpdata+[np.log10(totalcount*float(args.tgthighres[1:])),np.log10(totalcount)]).unsqueeze(0).cuda() - elif args.tgthighres[0] == 'a': - pretrain_gene_x = torch.tensor(tmpdata+[np.log10(totalcount)+float(args.tgthighres[1:]),np.log10(totalcount)]).unsqueeze(0).cuda() - elif args.tgthighres[0] == 't': - pretrain_gene_x = torch.tensor(tmpdata+[float(args.tgthighres[1:]),np.log10(totalcount)]).unsqueeze(0).cuda() - else: - raise ValueError('tgthighres must be start with f, a or t') - data_gene_ids = torch.arange(19266, device=pretrain_gene_x.device).repeat(pretrain_gene_x.shape[0], 1) - - value_labels = pretrain_gene_x > 0 - x, x_padding = gatherData(pretrain_gene_x, value_labels, pretrainconfig['pad_token_id']) - - #Cell embedding - if args.output_type=='cell': - position_gene_ids, _ = gatherData(data_gene_ids, value_labels, pretrainconfig['pad_token_id']) - x = pretrainmodel.token_emb(torch.unsqueeze(x, 2).float(), output_weight = 0) - position_emb = pretrainmodel.pos_emb(position_gene_ids) - x += position_emb - geneemb = pretrainmodel.encoder(x,x_padding) - - geneemb1 = geneemb[:,-1,:] - geneemb2 = geneemb[:,-2,:] - geneemb3, _ = torch.max(geneemb[:,:-2,:], dim=1) - geneemb4 = torch.mean(geneemb[:,:-2,:], dim=1) - if args.pool_type=='all': - geneembmerge = torch.concat([geneemb1,geneemb2,geneemb3,geneemb4],axis=1) - elif args.pool_type=='max': - geneembmerge, _ = torch.max(geneemb, dim=1) - else: - raise ValueError('pool_type must be all or max') - geneexpemb.append(geneembmerge.detach().cpu().numpy()) - - #Gene embedding - elif args.output_type=='gene': - pretrainmodel.to_final = None - encoder_data, encoder_position_gene_ids, encoder_data_padding, encoder_labels, decoder_data, decoder_data_padding, new_data_raw, data_mask_labels, decoder_position_gene_ids = getEncoerDecoderData(pretrain_gene_x.float(),pretrain_gene_x.float(),pretrainconfig) - out = pretrainmodel.forward(x=encoder_data, padding_label=encoder_data_padding, - encoder_position_gene_ids=encoder_position_gene_ids, - encoder_labels=encoder_labels, - decoder_data=decoder_data, - mask_gene_name=False, - mask_labels=None, - decoder_position_gene_ids=decoder_position_gene_ids, - decoder_data_padding_labels=decoder_data_padding, - ) - out = out[:,:19264,:].contiguous() - geneexpemb.append(out.detach().cpu().numpy()) - - #Gene batch embedding - elif args.output_type=='gene_batch': - batchcontainer.append(pretrain_gene_x.float()) - if len(batchcontainer)==gexpr_feature.shape[0]: - batchcontainer = torch.concat(batchcontainer,axis=0) - else: - continue - pretrainmodel.to_final = None - encoder_data, encoder_position_gene_ids, encoder_data_padding, encoder_labels, decoder_data, decoder_data_padding, new_data_raw, data_mask_labels, decoder_position_gene_ids = getEncoerDecoderData(batchcontainer,batchcontainer,pretrainconfig) - out = pretrainmodel.forward(x=encoder_data, padding_label=encoder_data_padding, - encoder_position_gene_ids=encoder_position_gene_ids, - encoder_labels=encoder_labels, - decoder_data=decoder_data, - mask_gene_name=False, - mask_labels=None, - decoder_position_gene_ids=decoder_position_gene_ids, - decoder_data_padding_labels=decoder_data_padding, - ) - geneexpemb = out[:,:19264,:].contiguous().detach().cpu().numpy() - #Gene_expression - elif args.output_type=='gene_expression': - encoder_data, encoder_position_gene_ids, encoder_data_padding, encoder_labels, decoder_data, decoder_data_padding, new_data_raw, data_mask_labels, decoder_position_gene_ids = getEncoerDecoderData(pretrain_gene_x.float(),pretrain_gene_x.float(),pretrainconfig) - out = pretrainmodel.forward(x=encoder_data, padding_label=encoder_data_padding, - encoder_position_gene_ids=encoder_position_gene_ids, - encoder_labels=encoder_labels, - decoder_data=decoder_data, - mask_gene_name=False, - mask_labels=None, - decoder_position_gene_ids=decoder_position_gene_ids, - decoder_data_padding_labels=decoder_data_padding, - ) - out = out[:,:19264].contiguous() - geneexpemb.append(out.detach().cpu().numpy()) - else: - raise ValueError('output_type must be cell or gene or gene_batch or gene_expression') - geneexpemb = np.squeeze(np.array(geneexpemb)) - print(geneexpemb.shape) - np.save(strname,geneexpemb) - - return geneexpemb - diff --git a/src/utils/load.py b/src/utils/load.py deleted file mode 100644 index fe26ae0b..00000000 --- a/src/utils/load.py +++ /dev/null @@ -1,198 +0,0 @@ -# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited - -import torch -import sys -import os -import numpy as np -import random -from pretrainmodels import select_model -import math - -def next_16x(x): - return int(math.ceil(x / 16) * 16) - -def seed_all(seed, cuda_deterministic=False): - random.seed(seed) - os.environ['PYTHONHASHSEED'] = str(seed) - np.random.seed(seed) - torch.manual_seed(seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - if cuda_deterministic: # slower, more reproducible - torch.backends.cudnn.deterministic = True - torch.backends.cudnn.benchmark = False - else: - torch.backends.cudnn.deterministic = False - torch.backends.cudnn.benchmark = True - -def gatherDatanopad(data, labels, pad_token_id): - max_num = labels.sum(1) - none_labels = ~labels - labels = labels.float() - labels[none_labels] = torch.tensor(-float('Inf'), device=labels.device) - - tmp_data = torch.tensor([(i + 1) * 20000 for i in range(labels.shape[1], 0, -1)], device=labels.device) - labels += tmp_data - - - fake_label_gene_idx = labels.topk(max_num).indices - - new_data = torch.gather(data, 1, fake_label_gene_idx) - - padding_labels = (new_data == pad_token_id) - - return new_data, padding_labels - -def gatherData(data, labels, pad_token_id): - value_nums = labels.sum(1) - max_num = max(value_nums) - - - fake_data = torch.full((data.shape[0], max_num), pad_token_id, - device=data.device) - data = torch.hstack([data, fake_data]) - - fake_label = torch.full((labels.shape[0], max_num), 1, - device=labels.device) - none_labels = ~labels - labels = labels.float() - labels[none_labels] = torch.tensor(-float('Inf'), device=labels.device) - - tmp_data = torch.tensor([(i + 1) * 20000 for i in range(labels.shape[1], 0, -1)], device=labels.device) - labels += tmp_data - - labels = torch.hstack([labels, fake_label]) - - fake_label_gene_idx = labels.topk(max_num).indices - - new_data = torch.gather(data, 1, fake_label_gene_idx) - - padding_labels = (new_data == pad_token_id) - - return new_data, padding_labels - - -def convertconfig(ckpt): - newconfig = {} - newconfig['config']={} - model_type = ckpt['config']['model'] - - for key, val in ckpt['config']['model_config'][model_type].items(): - newconfig['config'][key]=val - - for key, val in ckpt['config']['dataset_config']['rnaseq'].items(): - newconfig['config'][key]=val - - if model_type == 'performergau_resolution': - model_type = 'performer_gau' - - import collections - d = collections.OrderedDict() - for key, val in ckpt['state_dict'].items(): - d[str(key).split('model.')[1]]=val - - newconfig['config']['model_type']=model_type - newconfig['model_state_dict']=d - newconfig['config']['pos_embed']=False - newconfig['config']['device']='cuda' - return newconfig - -def load_model(best_ckpt_path, device): - model_data = torch.load(best_ckpt_path,map_location=device) - if not model_data.__contains__('config'): - print('***** No config *****') - config={} - config['model_type']='flash_all' - else: - config=model_data['config'] - print(config) - if not config.__contains__('qv_dim'): - if config['model'] != 'mae_autobin': - if config.__contains__('dim_head'): - config['qv_dim']=config['dim_head'] - else: - print('***** No qv_dim ***** set 64') - config['qv_dim']= 64 - if not config.__contains__('ppi_edge'): - config['ppi_edge']=None - model = select_model(config) - model_state_dict = model_data['model_state_dict'] - model.load_state_dict(model_state_dict) - return model.cuda(),config - -def load_model_frommmf(best_ckpt_path, key='gene'): - model_data = torch.load(best_ckpt_path,map_location='cpu') - model_data = model_data[key] - model_data = convertconfig(model_data) - if not model_data.__contains__('config'): - print('***** No config *****') - config={} - config['model_type']='flash_all' - else: - config=model_data['config'] - print(config) - if not config.__contains__('qv_dim'): - if config['model'] != 'mae_autobin': - if config.__contains__('dim_head'): - config['qv_dim']=config['dim_head'] - else: - print('***** No qv_dim ***** set 64') - config['qv_dim']= 64 - if not config.__contains__('ppi_edge'): - config['ppi_edge']=None - model = select_model(config) - model_state_dict = model_data['model_state_dict'] - model.load_state_dict(model_state_dict) - return model.cuda(),config - -def main_gene_selection(X_df, gene_list): - """ - Describe: - rebuild the input adata to select target genes encode protein - Parameters: - adata->`~anndata.AnnData` object: adata with var index_name by gene symbol - gene_list->list: wanted target gene - Returns: - adata_new->`~anndata.AnnData` object - to_fill_columns->list: zero padding gene - """ - to_fill_columns = list(set(gene_list) - set(X_df.columns)) - padding_df = pd.DataFrame(np.zeros((X_df.shape[0], len(to_fill_columns))), - columns=to_fill_columns, - index=X_df.index) - X_df = pd.DataFrame(np.concatenate([df.values for df in [X_df, padding_df]], axis=1), - index=X_df.index, - columns=list(X_df.columns) + list(padding_df.columns)) - X_df = X_df[gene_list] - - var = pd.DataFrame(index=X_df.columns) - var['mask'] = [1 if i in to_fill_columns else 0 for i in list(var.index)] - return X_df, to_fill_columns - -def get_genename(): - gene_list_df = pd.read_csv('./OS_scRNA_gene_index.19264.tsv', header=0, delimiter='\t') - return list(gene_list_df['gene_name']) - -def save_AUCs(AUCs, filename): - with open(filename, 'a') as f: - f.write('\t'.join(map(str, AUCs)) + '\n') - -def getEncoerDecoderData(data, data_raw, config): - decoder_data = data.clone().detach() - decoder_data_padding = torch.full_like(data, False, dtype=torch.bool).to(data.device) - - encoder_data_labels = data_raw > 0 - encoder_data, encoder_data_padding = gatherData(decoder_data, encoder_data_labels, - config['pad_token_id']) - new_data_raw = data_raw - data_gene_ids = torch.arange(data.shape[1], device=data.device).repeat(data.shape[0], 1) - encoder_position_gene_ids, _ = gatherData(data_gene_ids, encoder_data_labels, - config['pad_token_id']) - decoder_position_gene_ids = data_gene_ids - data_mask_labels = None - - encoder_position_gene_ids[encoder_data_padding] = config["seq_len"] - decoder_position_gene_ids[decoder_data_padding] = config["seq_len"] - - return encoder_data, encoder_position_gene_ids, encoder_data_padding, encoder_data_labels, decoder_data, decoder_data_padding, new_data_raw, data_mask_labels, decoder_position_gene_ids diff --git a/src/utils/pretrainmodels/__init__.py b/src/utils/pretrainmodels/__init__.py deleted file mode 100644 index f147901f..00000000 --- a/src/utils/pretrainmodels/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited - -from pretrainmodels.select_model import select_model diff --git a/src/utils/pretrainmodels/mae_autobin.py b/src/utils/pretrainmodels/mae_autobin.py deleted file mode 100644 index 953b4c91..00000000 --- a/src/utils/pretrainmodels/mae_autobin.py +++ /dev/null @@ -1,162 +0,0 @@ -# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited - -import math -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn -from torch.cuda.amp import autocast -from einops import rearrange, repeat - -from functools import partial -from contextlib import contextmanager - - -def exists(val): - return val is not None - -class AutoDiscretizationEmbedding2(nn.Module): - def __init__(self, dim, max_seq_len, bin_num, bin_alpha, mask_token_id = None, pad_token_id = None): - super().__init__() - - self.dim = dim - self.max_seq_len = max_seq_len - self.bin_num = bin_num - self.bin_alpha = bin_alpha - - self.mlp = nn.Linear(1, self.bin_num) - self.mlp2 = nn.Linear(self.bin_num, self.bin_num) - self.LeakyReLU = nn.LeakyReLU(0.1) - self.Softmax = nn.Softmax(dim=-1) - self.emb = nn.Embedding(self.bin_num, self.dim) - - self.emb_mask = nn.Embedding(1, self.dim) - self.emb_pad = nn.Embedding(1, self.dim) - - self.bin_num_idx = torch.tensor(range(self.bin_num)) - self.mask_token_id = mask_token_id - self.pad_token_id = pad_token_id - # print('self.bin_num_idx',self.bin_num_idx, self.bin_num_idx.shape) - - self.tensor0 = torch.tensor(0, dtype=torch.long) - - def forward(self, x, output_weight=0): - x_mask_idx = (x==self.mask_token_id).nonzero() - x_pad_idx = (x==self.pad_token_id).nonzero() - # print("x_mask",x_mask_idx.shape,x_mask_idx) - - x = self.mlp(x) # [B,N,1] -> [B,N,H] - x = self.LeakyReLU(x) # [B,N,H] - x_crosslayer = self.mlp2(x) # [B,N,H] - x = self.bin_alpha * x + x_crosslayer # [B,N,H] - weight = self.Softmax(x) # [B, N, H] - # print('weight', weight.shape, weight, torch.sum(weight, 2)) - - bin_num_idx = self.bin_num_idx.to(x.device) # [H,] - # print('bin_num_idx', bin_num_idx.shape) - - token_emb = self.emb(bin_num_idx) # [H, D] - # print('token_emb', token_emb.shape) - x = torch.matmul(weight, token_emb) #[B, N, D] - - # print("x_emb",x.shape,x) - - tensor0 = torch.tensor(0, dtype=torch.long, device=x.device) - - mask_token_emb = self.emb_mask(tensor0).to(x.device).type(x.dtype) - # print(mask_token_emb.dtype) - # print("x", x.dtype) - x[x_mask_idx[:,0],x_mask_idx[:,1],:] = mask_token_emb.repeat(x_mask_idx.shape[0],1) - # print("x_emb",x.shape,x) - - pad_token_emb = self.emb_pad(tensor0).to(x.device).type(x.dtype) - x[x_pad_idx[:,0],x_pad_idx[:,1],:] = pad_token_emb.repeat(x_pad_idx.shape[0],1) - - if output_weight: - return x,weight - return x - -class RandomPositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - self.emb = nn.Embedding(max_seq_len, dim) - - def forward(self, x): - t = torch.arange(x.shape[1], device=x.device) - return self.emb(t) - - -class MaeAutobin(nn.Module): - def __init__( - self, - *, - num_tokens, # num of tokens - max_seq_len, # max length of sequence - embed_dim, # encoder dim of tokens - decoder_embed_dim, - tie_embed=False, - bin_alpha = 1.0, - bin_num = 10, - pad_token_id = None, - mask_token_id = None, - ): - super(MaeAutobin, self).__init__() - - self.max_seq_len = max_seq_len - self.num_tokens = num_tokens - self.pad_token_id = pad_token_id - self.mask_token_id = mask_token_id - - # encoder - self.token_emb = AutoDiscretizationEmbedding2(embed_dim, max_seq_len, bin_num=bin_num, bin_alpha=bin_alpha, pad_token_id=self.pad_token_id, mask_token_id=self.mask_token_id) - self.pos_emb = nn.Embedding(max_seq_len+1, embed_dim) #RandomPositionalEmbedding(embed_dim, max_seq_len) - - # ## DEBUG - self.encoder = None - - ##### decoder - self.decoder = None - self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) - self.norm = nn.LayerNorm(decoder_embed_dim) - self.to_final = nn.Linear(decoder_embed_dim, 1) - - def forward(self, x, padding_label, encoder_position_gene_ids, encoder_labels, decoder_data, - mask_gene_name, mask_labels, decoder_position_gene_ids, decoder_data_padding_labels, - output_attentions=False, **kwargs): - b, n, device = *x.shape, x.device - assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' - - # token and positional embedding - x = self.token_emb(torch.unsqueeze(x, 2), output_weight = 0) - if output_attentions: - x.requires_grad_() # used for attn_map output - - position_emb = self.pos_emb(encoder_position_gene_ids) - x += position_emb - x = self.encoder(x, padding_mask=padding_label) - - decoder_data = self.token_emb(torch.unsqueeze(decoder_data, 2)) - position_emb = self.pos_emb(decoder_position_gene_ids) - if mask_gene_name: - # todo - # mask gene_name - print('mask_gene_name not done') - exit(0) - batch_idx, gen_idx = (encoder_labels == True).nonzero(as_tuple=True) - decoder_data[batch_idx, gen_idx] = x[~padding_label].to(decoder_data.dtype) - - decoder_data += position_emb - - decoder_data = self.decoder_embed(decoder_data) - x = self.decoder(decoder_data, padding_mask=decoder_data_padding_labels) - - # print("x0",x.shape) - x = self.norm(x) - # print("x1",x.shape) - if exists(self.to_final): - x = self.to_final(x) - return x.squeeze(2) - else: - return x - - return x diff --git a/src/utils/pretrainmodels/performer.py b/src/utils/pretrainmodels/performer.py deleted file mode 100644 index 500679d1..00000000 --- a/src/utils/pretrainmodels/performer.py +++ /dev/null @@ -1,638 +0,0 @@ -## Modeified code from https://github.com/TencentAILabHealthcare/scBERT -import math -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn -from torch.cuda.amp import autocast -from einops import rearrange, repeat - -from functools import partial -from contextlib import contextmanager - -from local_attention import LocalAttention -from pretrainmodels.reversible import ReversibleSequence, SequentialSequence - -try: - from apex import amp - APEX_AVAILABLE = True -except: - APEX_AVAILABLE = False - -# helpers - -def exists(val): - return val is not None - -def empty(tensor): - return tensor.numel() == 0 - -def default(val, d): - return val if exists(val) else d - -@contextmanager -def null_context(): - yield - -def cast_tuple(val): - return (val,) if not isinstance(val, tuple) else val - -# def get_module_device(module): -# return next(module.parameters).device - -def get_module_device(module): - try: - return next(module.parameters()).device - except StopIteration: - # For nn.DataParallel compatibility in PyTorch 1.5 - def find_tensor_attributes(module): - tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)] - return tuples - gen = module._named_members(get_members_fn=find_tensor_attributes) - first_tuple = next(gen) - return first_tuple[1].device - -def find_modules(nn_module, type): - return [module for module in nn_module.modules() if isinstance(module, type)] - -class Always(nn.Module): - def __init__(self, val): - super().__init__() - self.val = val - - def forward(self, *args, **kwargs): - return self.val - -# kernel functions - -# transcribed from jax to pytorch from -# https://github.com/google-research/google-research/blob/master/performer/fast_attention/jax/fast_attention.py - -def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device = None): - b, h, *_ = data.shape - - data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1. - - ratio = (projection_matrix.shape[0] ** -0.5) - - projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h) - projection = projection.type_as(data) - - data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection) - - diag_data = data ** 2 - diag_data = torch.sum(diag_data, dim=-1) - diag_data = (diag_data / 2.0) * (data_normalizer ** 2) - diag_data = diag_data.unsqueeze(dim=-1) - - if is_query: - data_dash = ratio * ( - torch.exp(data_dash - diag_data - - torch.max(data_dash, dim=-1, keepdim=True).values) + eps) - else: - data_dash = ratio * ( - torch.exp(data_dash - diag_data - torch.max(data_dash)) + eps) - - return data_dash.type_as(data) - -def generalized_kernel(data, *, projection_matrix, kernel_fn = nn.ReLU(), kernel_epsilon = 0.001, normalize_data = True, device = None): - b, h, *_ = data.shape - - data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1. - - if projection_matrix is None: - return kernel_fn(data_normalizer * data) + kernel_epsilon - - projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h) - projection = projection.type_as(data) - - data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection) - - data_prime = kernel_fn(data_dash) + kernel_epsilon - return data_prime.type_as(data) - -def orthogonal_matrix_chunk(cols, device = None): - unstructured_block = torch.randn((cols, cols), device = device) - q, r = torch.linalg.qr(unstructured_block.cpu(), mode = 'reduced') - q, r = map(lambda t: t.to(device), (q, r)) - return q.t() - -def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling = 0, device = None): - nb_full_blocks = int(nb_rows / nb_columns) - - block_list = [] - - for _ in range(nb_full_blocks): - q = orthogonal_matrix_chunk(nb_columns, device = device) - block_list.append(q) - - remaining_rows = nb_rows - nb_full_blocks * nb_columns - if remaining_rows > 0: - q = orthogonal_matrix_chunk(nb_columns, device = device) - block_list.append(q[:remaining_rows]) - - final_matrix = torch.cat(block_list) - - if scaling == 0: - multiplier = torch.randn((nb_rows, nb_columns), device = device).norm(dim = 1) - elif scaling == 1: - multiplier = math.sqrt((float(nb_columns))) * torch.ones((nb_rows,), device = device) - else: - raise ValueError(f'Invalid scaling {scaling}') - - return torch.diag(multiplier) @ final_matrix - -# linear attention classes with softmax kernel - -# non-causal linear attention -def linear_attention(q, k, v): - k_cumsum = k.sum(dim = -2) - D_inv = 1. / torch.einsum('...nd,...d->...n', q, k_cumsum.type_as(q)) - context = torch.einsum('...nd,...ne->...de', k, v) - out = torch.einsum('...de,...nd,...n->...ne', context, q, D_inv) - return out - -# efficient causal linear attention, created by EPFL -def causal_linear_attention(q, k, v, eps = 1e-6): - from fast_transformers.causal_product import CausalDotProduct - autocast_enabled = torch.is_autocast_enabled() - is_half = isinstance(q, torch.cuda.HalfTensor) - assert not is_half or APEX_AVAILABLE, 'half tensors can only be used if nvidia apex is available' - cuda_context = null_context if not autocast_enabled else partial(autocast, enabled = False) - - causal_dot_product_fn = amp.float_function(CausalDotProduct.apply) if is_half else CausalDotProduct.apply - - k_cumsum = k.cumsum(dim=-2) + eps - D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q)) - - with cuda_context(): - if autocast_enabled: - q, k, v = map(lambda t: t.float(), (q, k, v)) - - out = causal_dot_product_fn(q, k, v) - - out = torch.einsum('...nd,...n->...nd', out, D_inv) - return out - -# inefficient causal linear attention, without cuda code, for reader's reference -# not being used -def causal_linear_attention_noncuda(q, k, v, chunk_size = 128): - last_k_cumsum = 0 - last_context_cumsum = 0 - outs = [] - - for q, k, v in zip(*map(lambda t: t.chunk(chunk_size, dim = -2), (q, k, v))): - k_cumsum = last_k_cumsum + k.cumsum(dim=-2) - - D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q)) - context = torch.einsum('...nd,...ne->...nde', k, v) - context_cumsum = last_context_cumsum + context.cumsum(dim=-3) - out = torch.einsum('...nde,...nd,...n->...ne', context_cumsum, q, D_inv) - - last_k_cumsum = k_cumsum[:, :, -1:] - last_context_cumsum = context_cumsum[:, :, -1:] - outs.append(out) - - return torch.cat(outs, dim = -2) - -def norm_tensor(tensor, dim=-1): - return tensor / tensor.sum(dim=dim).unsqueeze(dim) - -class FastAttention(nn.Module): - def __init__(self, dim_heads, nb_features = None, ortho_scaling = 0, causal = False, generalized_attention = False, kernel_fn = nn.ReLU(), no_projection = False): - super().__init__() - nb_features = default(nb_features, int(dim_heads * math.log(dim_heads))) - - self.dim_heads = dim_heads - self.nb_features = nb_features - self.ortho_scaling = ortho_scaling - - self.create_projection = partial(gaussian_orthogonal_random_matrix, nb_rows = self.nb_features, nb_columns = dim_heads, scaling = ortho_scaling) - projection_matrix = self.create_projection() - self.register_buffer('projection_matrix', projection_matrix) - - self.generalized_attention = generalized_attention - self.kernel_fn = kernel_fn - - self.no_projection = no_projection - - self.causal = causal - if causal: - try: - import fast_transformers.causal_product.causal_product_cuda - self.causal_linear_fn = partial(causal_linear_attention) - except ImportError: - print('unable to import cuda code for auto-regressive Performer. will default to the memory inefficient non-cuda version') - self.causal_linear_fn = causal_linear_attention_noncuda - - @torch.no_grad() - def redraw_projection_matrix(self, device): - projections = self.create_projection(device = device) - self.projection_matrix.copy_(projections) - del projections - - def forward(self, q, k, v, output_attentions = False): - device = q.device - if self.no_projection: - q = q.softmax(dim = -1) - k = torch.exp(k) if self.causal else k.softmax(dim = -2) - - elif self.generalized_attention: - create_kernel = partial(generalized_kernel, kernel_fn = self.kernel_fn, projection_matrix = self.projection_matrix, device = device) - q, k = map(create_kernel, (q, k)) - - else: - create_kernel = partial(softmax_kernel, projection_matrix = self.projection_matrix, device = device) - q = create_kernel(q, is_query = True) - k = create_kernel(k, is_query = False) - - attn_fn = linear_attention if not self.causal else self.causal_linear_fn - out = attn_fn(q, k, v) - if output_attentions: - v_diag = torch.eye(v.shape[-2]).to(device) - v_diag = v_diag.unsqueeze(0).unsqueeze(0).repeat(v.shape[0],v.shape[1],1,1) - attn_weights = torch.zeros(1, q.shape[1], q.shape[2], q.shape[2]).to('cpu').to(torch.float16) - for head_dim in range(q.shape[1]): - attn_weights[0, head_dim] = attn_fn(q[:,head_dim].to(torch.float16), k[:,head_dim].to(torch.float16), v_diag[:,head_dim].to(torch.float16)).detach().cpu() - attn_weights /= q.shape[1] - return out, attn_weights - else: - return out - -# classes - -class ReZero(nn.Module): - def __init__(self, fn): - super().__init__() - self.g = nn.Parameter(torch.tensor(1e-3)) - self.fn = fn - - def forward(self, x, **kwargs): - return self.fn(x, **kwargs) * self.g - -class PreScaleNorm(nn.Module): - def __init__(self, dim, fn, eps=1e-5): - super().__init__() - self.fn = fn - self.g = nn.Parameter(torch.ones(1)) - self.eps = eps - - def forward(self, x, **kwargs): - n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps) - x = x / n * self.g - return self.fn(x, **kwargs) - -class PreLayerNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.norm = nn.LayerNorm(dim) - self.fn = fn - def forward(self, x, **kwargs): - - return self.fn(self.norm(x), **kwargs) - -class Chunk(nn.Module): - def __init__(self, chunks, fn, along_dim = -1): - super().__init__() - self.dim = along_dim - self.chunks = chunks - self.fn = fn - - def forward(self, x, **kwargs): - if self.chunks == 1: - return self.fn(x, **kwargs) - chunks = x.chunk(self.chunks, dim = self.dim) - return torch.cat([self.fn(c, **kwargs) for c in chunks], dim = self.dim) - -class FeedForward(nn.Module): - def __init__(self, dim, mult = 4, dropout = 0., activation = None, glu = False): - super().__init__() - activation = default(activation, nn.GELU) - - self.glu = glu - self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1)) - self.act = activation() - self.dropout = nn.Dropout(dropout) - self.w2 = nn.Linear(dim * mult, dim) - - def forward(self, x, **kwargs): - if not self.glu: - x = self.w1(x) - x = self.act(x) - else: - x, v = self.w1(x).chunk(2, dim=-1) - x = self.act(x) * v - - x = self.dropout(x) - x = self.w2(x) - return x - -class SelfAttention(nn.Module): - def __init__( - self, - dim, - causal = False, - heads = 8, - dim_head = 64, - local_heads = 0, - local_window_size = 256, - nb_features = None, - feature_redraw_interval = 1000, - generalized_attention = False, - kernel_fn = nn.ReLU(), - dropout = 0., - no_projection = False, - qkv_bias = False - ): - super().__init__() - assert dim % heads == 0, 'dimension must be divisible by number of heads' - dim_head = default(dim_head, dim // heads) - inner_dim = dim_head * heads - self.fast_attention = FastAttention(dim_head, nb_features, causal = causal, generalized_attention = generalized_attention, kernel_fn = kernel_fn, no_projection = no_projection) - - self.heads = heads - self.global_heads = heads - local_heads - self.local_attn = LocalAttention(window_size = local_window_size, causal = causal, autopad = True, dropout = dropout, look_forward = int(not causal), rel_pos_emb_config = (dim_head, local_heads)) if local_heads > 0 else None - - self.to_q = nn.Linear(dim, inner_dim, bias = qkv_bias) - self.to_k = nn.Linear(dim, inner_dim, bias = qkv_bias) - self.to_v = nn.Linear(dim, inner_dim, bias = qkv_bias) - self.to_out = nn.Linear(inner_dim, dim) - self.dropout = nn.Dropout(dropout) - - def forward(self, x, pos_emb = None, context = None, mask = None, context_mask = None, output_attentions = False, **kwargs): - b, n, _, h, gh = *x.shape, self.heads, self.global_heads - - cross_attend = exists(context) - - context = default(context, x) - context_mask = default(context_mask, mask) if not cross_attend else context_mask - - q, k, v = self.to_q(x), self.to_k(context), self.to_v(context) - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v)) - (q, lq), (k, lk), (v, lv) = map(lambda t: (t[:, :gh], t[:, gh:]), (q, k, v)) - - attn_outs = [] - - if not empty(q): - if exists(context_mask): - global_mask = context_mask[:, None, :, None] - v.masked_fill_(~global_mask, 0.) - - if exists(pos_emb) and not cross_attend: - q, k, = apply_rotary_pos_emb(q, k, pos_emb) - - if output_attentions: - out, attn_weights = self.fast_attention(q, k, v, output_attentions) - else: - out = self.fast_attention(q, k, v) - attn_outs.append(out) - - if not empty(lq): - assert not cross_attend, 'local attention is not compatible with cross attention' - out = self.local_attn(lq, lk, lv, input_mask = mask) - attn_outs.append(out) - - out = torch.cat(attn_outs, dim = 1) # combine attn_out and cross_attn_out, here we have only attn_out, that means this line does nothing - out = rearrange(out, 'b h n d -> b n (h d)') - out = self.to_out(out) - if output_attentions: - return self.dropout(out), attn_weights - else: - return self.dropout(out) - -# positional embeddings - -class AbsolutePositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - self.emb = nn.Embedding(max_seq_len, dim) - - def forward(self, x): - t = torch.arange(x.shape[1], device=x.device) - return self.emb(t) - -# rotary positional embedding helpers - -def rotate_every_two(x): - x = rearrange(x, '... (d j) -> ... d j', j = 2) - x1, x2 = x.unbind(dim = -1) - x = torch.stack((-x2, x1), dim = -1) - return rearrange(x, '... d j -> ... (d j)') - -def apply_rotary_pos_emb(q, k, sinu_pos): - sinu_pos = rearrange(sinu_pos, '() n (j d) -> n j d', j = 2) - sin, cos = sinu_pos.unbind(dim = -2) - sin, cos = map(lambda t: repeat(t, 'b n -> b (n j)', j = 2), (sin, cos)) - q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k)) - return q, k - -# sinusoidal positional embeddings - -class Gene2VecPositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - gene2vec_weight = np.load('../data/gene2vec_16906.npy') - gene2vec_weight = np.concatenate((gene2vec_weight, np.zeros((1, gene2vec_weight.shape[1]))), axis=0) - gene2vec_weight = torch.from_numpy(gene2vec_weight) - self.emb = nn.Embedding.from_pretrained(gene2vec_weight) - - def forward(self, x): - t = torch.arange(x.shape[1], device=x.device) - print("t", t) - return self.emb(t) - -class Gene2VecPositionalEmbeddingIdx(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - gene2vec_weight = np.load('/nfs_beijing/minsheng/scbig/data/biomap_gene2vec.npy') - gene2vec_weight = np.concatenate((np.zeros((1, gene2vec_weight.shape[1])),gene2vec_weight),axis=0) - gene2vec_weight = gene2vec_weight[:,:dim] - gene2vec_weight = torch.Tensor(gene2vec_weight) - self.emb = nn.Embedding.from_pretrained(gene2vec_weight,freeze=False) - - def forward(self, x): - return self.emb(x) - - -class RandomPositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - gene2vec_weight = np.random.rand(max_seq_len-1, dim) - gene2vec_weight = np.concatenate((gene2vec_weight, np.zeros((1, gene2vec_weight.shape[1]))), axis=0) - gene2vec_weight = torch.from_numpy(gene2vec_weight) - self.emb = nn.Embedding.from_pretrained(gene2vec_weight, freeze=False) - - def forward(self, x): - t = torch.arange(x.shape[1], device=x.device) - return self.emb(t) - - -class RandomPositionalEmbeddingIdx(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - gene2vec_weight = np.random.rand(max_seq_len, dim) - gene2vec_weight = np.concatenate((np.zeros((1, gene2vec_weight.shape[1])),gene2vec_weight),axis=0) - gene2vec_weight = torch.from_numpy(gene2vec_weight) - self.emb = nn.Embedding.from_pretrained(gene2vec_weight, freeze=False) - - def forward(self, x): - return self.emb(x) - -# performer - -class Performer(nn.Module): - def __init__( - self, - dim, # dimension - depth, # layers - heads, # heads - dim_head, # dim of head - local_attn_heads = 0, # num of local attention heads, (heads - local_attn_heads) is num of global performers - local_window_size = 256, # window size of local attention - causal = False, # autoregressive or not - ff_mult = 4, # dim of intermediate features after attention / dim of input features - nb_features = None, # number of random features, if not set, will default to (d * log(d)), where d is the dimension of each head ?? what is random feature ?? - feature_redraw_interval = 1000, # how frequently to redraw the projection matrix, the more frequent, the slower the training - reversible = False, # reversible layers, from Reformer (save memory) - ff_chunks = 1, # chunk feedforward layer, from Reformer - generalized_attention = False, # defaults to softmax approximation, but can be set to True for generalized attention ?? what is generalized attention ?? - kernel_fn = nn.ReLU(), # the kernel function to be used, if generalized attention is turned on, defaults to Relu - use_scalenorm = False, # use scale norm, from 'Transformers without Tears' paper, a substitute for LayerNorm, priority: scalenorm.rezero.layernorm - use_rezero = False, # use Rezero or not, from 'Rezero is all you need' paper, a substitute for LayerNorm, priority: scalenorm.rezero.layernorm - ff_glu = False, # use GLU (Gated Linear Units) variant for feedforward - ff_dropout = 0., # feedforward dropout - attn_dropout = 0., # post-attention dropout - cross_attend = False, # ?? - no_projection = False, # ?? - auto_check_redraw = True, # ?? - qkv_bias = True, # ?? - ): - super().__init__() - layers = nn.ModuleList([]) - local_attn_heads = cast_tuple(local_attn_heads) - local_attn_heads = local_attn_heads * depth if len(local_attn_heads) == 1 else local_attn_heads - assert len(local_attn_heads) == depth, 'tuple specifying number of local attention heads per depth must be equal to the total depth' - assert all(map(lambda n: n >= 0 and n <= heads, local_attn_heads)), 'local attention head value must be less than the total number of heads' - - if use_scalenorm: - wrapper_fn = partial(PreScaleNorm, dim) - elif use_rezero: - wrapper_fn = ReZero - else: - wrapper_fn = partial(PreLayerNorm, dim) - - for _, local_heads in zip(range(depth), local_attn_heads): - layers.append(nn.ModuleList([ - wrapper_fn(SelfAttention(dim, causal = causal, heads = heads, dim_head = dim_head, local_heads = local_heads, local_window_size = local_window_size, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection, qkv_bias = qkv_bias)), - wrapper_fn(Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1)) - ])) - # if no need cross_attend(decoder), begin next cycle - if not cross_attend: - continue - layers.append(nn.ModuleList([ - wrapper_fn(SelfAttention(dim, heads = heads, dim_head = dim_head, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection)), - wrapper_fn(Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1)) - ])) - - execute_type = ReversibleSequence if reversible else SequentialSequence - - route_attn = ((True, False),) * depth * (2 if cross_attend else 1) # ((True, False), (True, False), (True, False), (True, False), (True, False), (True, False)) - route_context = ((False, False), (True, False)) * depth - attn_route_map = {'mask': route_attn, 'pos_emb': route_attn} - context_route_map = {'context': route_context, 'context_mask': route_context} if cross_attend else {} - self.net = execute_type(layers, args_route = {**attn_route_map, **context_route_map}) - - # keeping track of when to redraw projections for all attention layers - self.auto_check_redraw = auto_check_redraw - self.feature_redraw_interval = feature_redraw_interval - self.register_buffer('calls_since_last_redraw', torch.tensor(0)) - - def fix_projection_matrices_(self): - self.feature_redraw_interval = None - - def check_redraw_projections(self): - if not self.training: - return - - if exists(self.feature_redraw_interval) and self.calls_since_last_redraw >= self.feature_redraw_interval: - device = get_module_device(self) - - fast_attentions = find_modules(self, FastAttention) - for fast_attention in fast_attentions: - fast_attention.redraw_projection_matrix(device) - - self.calls_since_last_redraw.zero_() - return - - self.calls_since_last_redraw += 1 - - def forward(self, x, output_attentions = False, **kwargs): - if self.auto_check_redraw: - self.check_redraw_projections() - return self.net(x, output_attentions = output_attentions, **kwargs) - -class PerformerModule(nn.Module): - def __init__( - self, - max_seq_len, # max length of sequence - dim, # dim of tokens - depth, # layers - heads, # num of heads - dim_head=64, # dim of heads - local_attn_heads=0, - local_window_size=256, - causal=False, - ff_mult=4, - nb_features=None, - feature_redraw_interval=1000, - reversible=False, - ff_chunks=1, - ff_glu=False, - ff_dropout=0., - attn_dropout=0., - generalized_attention=False, - kernel_fn=nn.ReLU(), - use_scalenorm=False, - use_rezero=False, - cross_attend=False, - no_projection=False, - auto_check_redraw=True, - qkv_bias=True - ): - super(PerformerModule, self).__init__() - local_attn_heads = cast_tuple(local_attn_heads) - - self.max_seq_len = max_seq_len - - self.performer = Performer(dim, depth, heads, dim_head, local_attn_heads, local_window_size, causal, ff_mult, - nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, - kernel_fn, use_scalenorm, use_rezero, ff_glu, ff_dropout, attn_dropout, cross_attend, - no_projection, auto_check_redraw, qkv_bias) - self.norm = nn.LayerNorm(dim) - - def check_redraw_projections(self): - self.performer.check_redraw_projections() - - def fix_projection_matrices_(self): - self.performer.fix_projection_matrices_() - - def forward(self, x, output_attentions=False, **kwargs): - - b, n, _, device = *x.shape, x.device - assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' - # print('00 input b,n,device',b,n,device,x.shape) - - if output_attentions: - x, attn_weights = self.performer(x, output_attentions=output_attentions, **kwargs) - # norm and to logits - x = self.norm(x) - - return x, attn_weights - - else: - x = self.performer(x, output_attentions=output_attentions, **kwargs) - x = self.norm(x) - - return x diff --git a/src/utils/pretrainmodels/pytorchTransformer.py b/src/utils/pretrainmodels/pytorchTransformer.py deleted file mode 100644 index 48890d1d..00000000 --- a/src/utils/pretrainmodels/pytorchTransformer.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited - - -import torch -import torch.nn as nn -from .performer import Gene2VecPositionalEmbedding, RandomPositionalEmbedding - -from torch.nn import functional as F - - -class pytorchTransformerLM_mse(nn.Module): - def __init__(self, - num_tokens, - max_seq_len, - dim, - depth, - heads, - ff_mult=4, - emb_dropout=0., - g2v_position_emb=False - ): - super(pytorchTransformerLM_mse, self).__init__() - self.max_seq_len = max_seq_len - self.token_emb = nn.Embedding(num_tokens, dim) - - if g2v_position_emb: - self.pos_emb = Gene2VecPositionalEmbedding(dim, max_seq_len) - print("pos_emb Gene2Vec", self.pos_emb) - else: - self.pos_emb = RandomPositionalEmbedding(dim, max_seq_len) - print("pos_emb no ", self.pos_emb) - - self.dropout = nn.Dropout(emb_dropout) - - self.encoder_layer = nn.TransformerEncoderLayer(d_model=dim, - nhead=heads, dim_feedforward=dim*ff_mult) - self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=depth) - - self.norm = nn.LayerNorm(dim) - self.to_out = nn.Linear(dim, num_tokens) - self.to_final = nn.Linear(num_tokens, 1) - - def forward(self, x): - b, n, device = *x.shape, x.device - assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' - - # token and positional embedding - x = self.token_emb(x) - x += self.pos_emb(x) - - x = self.dropout(x) # embedding dropout - - # x = self.transformer_encoder(x) # get encodings [B, N, D] - x=torch.transpose(x, 0, 1) - x = self.transformer_encoder(x) - x = torch.transpose(x, 0, 1) - - # layernorm and to logits - x = self.norm(x) - x = self.to_out(x) # [B, N, C] - - if exists(self.to_final): - x = self.to_final(x) - return x.squeeze(2) # torch.Size([8, 13418]) - else: - return x - - return x - -def exists(val): - return val is not None - - -def _get_activation_fn(activation): - if activation == "relu": - return F.relu - elif activation == "gelu": - return F.gelu - - raise RuntimeError("activation should be relu/gelu, not {}".format(activation)) diff --git a/src/utils/pretrainmodels/reversible.py b/src/utils/pretrainmodels/reversible.py deleted file mode 100644 index 11fa00b7..00000000 --- a/src/utils/pretrainmodels/reversible.py +++ /dev/null @@ -1,200 +0,0 @@ -## Modeified Code from https://github.com/TencentAILabHealthcare/scBERT - -import torch -import torch.nn as nn -from operator import itemgetter -from torch.autograd.function import Function -from torch.utils.checkpoint import get_device_states, set_device_states - -# for routing arguments into the functions of the reversible layer -def route_args(router, args, depth): - routed_args = [(dict(), dict()) for _ in range(depth)] - matched_keys = [key for key in args.keys() if key in router] - - for key in matched_keys: - val = args[key] - for depth, ((f_args, g_args), routes) in enumerate(zip(routed_args, router[key])): - new_f_args, new_g_args = map(lambda route: ({key: val} if route else {}), routes) - routed_args[depth] = ({**f_args, **new_f_args}, {**g_args, **new_g_args}) - return routed_args - -# following example for saving and setting rng here https://pytorch.org/docs/stable/_modules/torch/utils/checkpoint.html -class Deterministic(nn.Module): - def __init__(self, net): - super().__init__() - self.net = net - self.cpu_state = None - self.cuda_in_fwd = None - self.gpu_devices = None - self.gpu_states = None - - def record_rng(self, *args): - self.cpu_state = torch.get_rng_state() - if torch.cuda._initialized: - self.cuda_in_fwd = True - self.gpu_devices, self.gpu_states = get_device_states(*args) - - def forward(self, *args, record_rng = False, set_rng = False, **kwargs): - if record_rng: - self.record_rng(*args) - - if not set_rng: - return self.net(*args, **kwargs) - - rng_devices = [] - if self.cuda_in_fwd: - rng_devices = self.gpu_devices - - with torch.random.fork_rng(devices=rng_devices, enabled=True): - torch.set_rng_state(self.cpu_state) - if self.cuda_in_fwd: - set_device_states(self.gpu_devices, self.gpu_states) - return self.net(*args, **kwargs) - -# heavily inspired by https://github.com/RobinBruegger/RevTorch/blob/master/revtorch/revtorch.py -# once multi-GPU is confirmed working, refactor and send PR back to source -class ReversibleBlock(nn.Module): - def __init__(self, f, g): - super().__init__() - self.f = Deterministic(f) - self.g = Deterministic(g) - - def forward(self, x, f_args = {}, g_args = {}): - x1, x2 = torch.chunk(x, 2, dim=2) - y1, y2 = None, None - - with torch.no_grad(): - y1 = x1 + self.f(x2, record_rng=self.training, **f_args) - y2 = x2 + self.g(y1, record_rng=self.training, **g_args) - - return torch.cat([y1, y2], dim=2) - - def backward_pass(self, y, dy, f_args = {}, g_args = {}): - y1, y2 = torch.chunk(y, 2, dim=2) - del y - - dy1, dy2 = torch.chunk(dy, 2, dim=2) - del dy - - with torch.enable_grad(): - y1.requires_grad = True - gy1 = self.g(y1, set_rng=True, **g_args) - torch.autograd.backward(gy1, dy2) - - with torch.no_grad(): - x2 = y2 - gy1 - del y2, gy1 - - dx1 = dy1 + y1.grad - del dy1 - y1.grad = None - - with torch.enable_grad(): - x2.requires_grad = True - fx2 = self.f(x2, set_rng=True, **f_args) - torch.autograd.backward(fx2, dx1, retain_graph=True) - - with torch.no_grad(): - x1 = y1 - fx2 - del y1, fx2 - - dx2 = dy2 + x2.grad - del dy2 - x2.grad = None - - x = torch.cat([x1, x2.detach()], dim=2) - dx = torch.cat([dx1, dx2], dim=2) - - return x, dx - -class _ReversibleFunction(Function): - @staticmethod - def forward(ctx, x, blocks, args): - ctx.args = args - for block, kwarg in zip(blocks, args): - x = block(x, **kwarg) - ctx.y = x.detach() - ctx.blocks = blocks - return x - - @staticmethod - def backward(ctx, dy): - y = ctx.y - args = ctx.args - for block, kwargs in zip(ctx.blocks[::-1], args[::-1]): - y, dy = block.backward_pass(y, dy, **kwargs) - return dy, None, None - -class SequentialSequence(nn.Module): - def __init__(self, layers, args_route = {}): - super().__init__() - assert all(len(route) == len(layers) for route in args_route.values()), 'each argument route map must have the same depth as the number of sequential layers' - self.layers = layers - self.args_route = args_route - - def forward(self, x, output_attentions = False, **kwargs): - args = route_args(self.args_route, kwargs, len(self.layers)) - layers_and_args = list(zip(self.layers, args)) - if output_attentions: - attn_weights = [] - for (f, g), (f_args, g_args) in layers_and_args: - # print(f,f_args) - if output_attentions: - x = x + f(x, output_attentions = output_attentions, **f_args)[0] - attn_weights.append(f(x, output_attentions = output_attentions, **f_args)[1].unsqueeze(0)) - else: - x = x + f(x, **f_args) - x = x + g(x, **g_args) - if output_attentions: - attn_weights = torch.transpose(torch.cat(attn_weights, dim=0), 0, 1) # the final dim is (batch, layer, head, len, len) - # attn_weights = torch.mean(attn_weights, dim=1) # the dim is (batch, head, len, len) - return x, attn_weights - else: - return x - -# GAU -class SequentialSequenceGAU(nn.Module): - def __init__(self, layers, args_route = {}): - super().__init__() - assert all(len(route) == len(layers) for route in args_route.values()), 'each argument route map must have the same depth as the number of sequential layers' - self.layers = layers - self.args_route = args_route - - def forward(self, x, output_attentions = False,ppi_edge = None, **kwargs): - args = route_args(self.args_route, kwargs, len(self.layers)) - layers_and_args = list(zip(self.layers, args)) - - if output_attentions: - attn_weights = [] - for f ,(f_args,g_args) in layers_and_args: - f = f[0] - # print(f,f_args) - if output_attentions: - tmpout = f(x, output_attentions = output_attentions,ppi_edge=ppi_edge, **f_args) - x = x + tmpout[0] - attn_weights.append(tmpout[1].unsqueeze(0)) - else: - x = x + f(x,ppi_edge=ppi_edge, **f_args) - if output_attentions: - attn_weights = torch.transpose(torch.cat(attn_weights, dim=0), 0, 1) # the final dim is (batch, layer, head, len, len) - # attn_weights = torch.mean(attn_weights, dim=1) # the dim is (batch, head, len, len) - return x, attn_weights - else: - return x - - -class ReversibleSequence(nn.Module): - def __init__(self, blocks, args_route = {}): - super().__init__() - self.args_route = args_route - self.blocks = nn.ModuleList([ReversibleBlock(f=f, g=g) for f, g in blocks]) - - def forward(self, x, **kwargs): - x = torch.cat([x, x], dim=-1) - - blocks = self.blocks - args = route_args(self.args_route, kwargs, len(blocks)) - args = list(map(lambda x: {'f_args': x[0], 'g_args': x[1]}, args)) - - out = _ReversibleFunction.apply(x, blocks, args) - return torch.stack(out.chunk(2, dim=-1)).sum(dim=0) diff --git a/src/utils/pretrainmodels/select_model.py b/src/utils/pretrainmodels/select_model.py deleted file mode 100644 index d2a781e6..00000000 --- a/src/utils/pretrainmodels/select_model.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited - - -from .transformer import pytorchTransformerModule -from .performer import PerformerModule -from .mae_autobin import MaeAutobin - -def select_module(config, sub_config, module_name): - if module_name == 'performer': - return PerformerModule( - max_seq_len=config['seq_len'], - dim=sub_config['hidden_dim'], - depth=sub_config['depth'], - heads=sub_config['heads'], - dim_head=sub_config['dim_head'], - ff_dropout=sub_config.get('ff_dropout',0.0), - attn_dropout=sub_config.get('attn_dropout',0.0) - ) - elif module_name == 'transformer': - return pytorchTransformerModule( - max_seq_len=config['seq_len'], - dim=sub_config['hidden_dim'], - depth=sub_config['depth'], - heads=sub_config['heads'] - ) - else: - print('module type error') - exit(0) - -def select_model(config): - if config["model"] == "mae_autobin": - encoder_config =config['encoder'] - decoder_config = config['decoder'] - encoder = select_module(config, encoder_config, config['encoder']['module_type']) - decoder = select_module(config, decoder_config, config['decoder']['module_type']) - model = MaeAutobin( - num_tokens=config['n_class'], - max_seq_len=config['seq_len'], - embed_dim=config['encoder']['hidden_dim'], - decoder_embed_dim=config['decoder']['hidden_dim'], - bin_alpha = config['bin_alpha'], - bin_num = config['bin_num'], - pad_token_id = config['pad_token_id'], - mask_token_id = config['mask_token_id'], - ) - model.encoder = encoder - model.decoder = decoder - else: - raise NotImplementedError("Unknown model type!") - return model - -def get_sub_config(config, target): - """ - 获取 包含 target 的 config - """ - sub_config = {} - for k in config.keys(): - if target in k: - tmp_name = k.replace(target + '_', '') - sub_config[tmp_name] = config[k] - return sub_config diff --git a/src/utils/pretrainmodels/transformer.py b/src/utils/pretrainmodels/transformer.py deleted file mode 100644 index f7ee5b9e..00000000 --- a/src/utils/pretrainmodels/transformer.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright 2023 BioMap (Beijing) Intelligence Technology Limited - - -import torch -import torch.nn as nn -import torch.nn.functional as F - -class pytorchTransformerModule(nn.Module): - def __init__(self, - max_seq_len, - dim, - depth, - heads, - ff_mult=4, - norm_first=False, - ): - super(pytorchTransformerModule, self).__init__() - - self.max_seq_len = max_seq_len - self.depth = depth - layers = [] - for i in range(depth): - layers.append(nn.TransformerEncoderLayer(d_model=dim, nhead=heads, - dim_feedforward=dim * ff_mult, - batch_first=True, - norm_first=norm_first, - #activation="gelu", - )) - - self.transformer_encoder = nn.ModuleList(layers) - self.norm = nn.LayerNorm(dim) - - def forward(self, x, padding_mask): - b, n, _, device = *x.shape, x.device - assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' - - # x get encodings [B, N, D] , batch_first is True - for mod in self.transformer_encoder: - x = mod(x, src_key_padding_mask=padding_mask) # , src_mask=mask, src_key_padding_mask=src_key_padding_mask) - # x = self.transformer_encoder(x) - x = self.norm(x) - - return x From 85a21a23105374d3e31176c2ff8551408e944cd2 Mon Sep 17 00:00:00 2001 From: HelloWorldLTY Date: Thu, 26 Dec 2024 15:56:10 -0500 Subject: [PATCH 05/16] update cellplm all --- CHANGELOG.md | 5 ---- src/methods/cellplm/config.vsh.yaml | 37 ++++++++--------------------- src/methods/cellplm/script.py | 24 ++++++++++--------- 3 files changed, 23 insertions(+), 43 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 603ab1bf..39710540 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,14 +6,9 @@ ## NEW FUNCTIONALITY -* Added `methods/cellplm` component. - -* Added `methods/scfoundation` component. - ## MAJOR CHANGES -* Updated scripts. diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index 5d285c7a..9a2ca0fd 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -1,15 +1,10 @@ -# The API specifies which type of component this is. -# It contains specifications for: -# - The input/output files -# - Common parameters -# - A unit test __merge__: ../../api/base_method.yaml # A unique identifier for your component (required). # Can contain only lowercase letters or underscores. name: cellplm # A relatively short label, used when rendering visualisations (required) -label: cellplm +label: CellPLM # A one sentence summary of how this method works (required). Used when # rendering summary tables. summary: "A foundation model pre-trained with cells as tokens." @@ -18,21 +13,11 @@ summary: "A foundation model pre-trained with cells as tokens." description: | CellPLM is a pre-trained language model specifically designed for single-cell analysis that leverages the principles of natural language processing (NLP) to understand and process single-cell gene expression data. references: - # doi: - # - https://openreview.net/forum?id=BKXvPDekud - bibtex: - - | - @inproceedings{ - wen2024cellplm, - title={Cell{PLM}: Pre-training of Cell Language Model Beyond Single Cells}, - author={Hongzhi Wen and Wenzhuo Tang and Xinnan Dai and Jiayuan Ding and Wei Jin and Yuying Xie and Jiliang Tang}, - booktitle={The Twelfth International Conference on Learning Representations}, - year={2024}, - url={https://openreview.net/forum?id=BKXvPDekud} - } + doi: + - https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1 links: # URL to the documentation for this method (required). - documentation: https://github.com/OmicsML/CellPLM + documentation: https://github.com/OmicsML/CellPLM/tree/main/tutorials # URL to the code repository for this method (required). repository: https://github.com/OmicsML/CellPLM @@ -41,12 +26,12 @@ info: method_types: [embedding] preferred_normalization: counts -# arguments: -# - name: --model -# type: string -# description: String giving the scGPT model to use -# choices: ["cellplm"] -# default: "cellplm" +arguments: + - name: --model + type: string + description: String giving the CellPLM model to use + choices: ["20231027_85M"] + default: "20231027_85M" # - name: --n_hvg # type: integer # default: 3000 @@ -64,11 +49,9 @@ engines: setup: - type: python pypi: - - torch==1.13.0 - gdown - scgpt # Install from PyPI to get dependencies - cellplm - - scanpy runners: - type: executable diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index 58c8cc3f..5feaef46 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -17,27 +17,32 @@ # Note: this section is auto-generated by viash at runtime. To edit it, make changes # in config.vsh.yaml and then run `viash config inject config.vsh.yaml`. - -set_seed(24) -PRETRAIN_VERSION = '20231027_85M' -DEVICE = "cuda" if torch.cuda.is_available() else "cpu" - par = { 'input': 'resources_test/.../input.h5ad', - 'output': 'output.h5ad' + 'output': 'output.h5ad', + "model": "20231027_85M", } meta = { 'name': 'cellplm' } ## VIASH END -# print(f"====== scGPT version {PRETRAIN_VERSION} ======", flush=True) sys.path.append(meta["resources_dir"]) from read_anndata_partial import read_anndata +set_seed(24) +PRETRAIN_VERSION = par['model'] +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" + print("\n>>> Reading input files...", flush=True) print(f"Input H5AD file: '{par['input']}'", flush=True) -adata = read_anndata(par["input"], X="layers/counts", obs="obs", var="var", uns="uns") +adata = read_anndata( + par['input'], + X='layers/normalized', + obs='obs', + var='var', + uns='uns' +) if adata.uns["dataset_organism"] != "homo_sapiens": raise ValueError( @@ -49,8 +54,6 @@ print('Preprocess data', flush=True) # ... preprocessing ... -sc.pp.normalize_total(adata) -sc.pp.log1p(adata) print('Train model', flush=True) # ... train model ... @@ -61,7 +64,6 @@ gdown.download_folder(drive_path, output=model_dir.name, quiet=True) print(f"Model directory: '{model_dir.name}'", flush=True) -PRETRAIN_VERSION = '20231027_85M' pipeline = CellEmbeddingPipeline(pretrain_prefix=PRETRAIN_VERSION, # Specify the pretrain checkpoint to load pretrain_directory=model_dir.name) From ff325ac12f9770a4a27f44aa3e361cc67e7468ff Mon Sep 17 00:00:00 2001 From: HelloWorldLTY Date: Thu, 26 Dec 2024 16:33:13 -0500 Subject: [PATCH 06/16] update doi --- src/methods/cellplm/config.vsh.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index 9a2ca0fd..393f5ce2 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -14,7 +14,7 @@ description: | CellPLM is a pre-trained language model specifically designed for single-cell analysis that leverages the principles of natural language processing (NLP) to understand and process single-cell gene expression data. references: doi: - - https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1 + - 10.1101/2023.10.03.560734 links: # URL to the documentation for this method (required). documentation: https://github.com/OmicsML/CellPLM/tree/main/tutorials From c3c03a64a0c95c3dacdcf7ff9119456f6c9be10c Mon Sep 17 00:00:00 2001 From: HelloWorldLTY Date: Tue, 7 Jan 2025 13:37:30 -0500 Subject: [PATCH 07/16] update all: --- CHANGELOG.md | 12 ++++++++++++ src/methods/cellplm/config.vsh.yaml | 22 +++++++--------------- src/methods/cellplm/script.py | 3 --- 3 files changed, 19 insertions(+), 18 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 39710540..2ba5cf94 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,9 +6,21 @@ ## NEW FUNCTIONALITY +* Added `control_methods/true_labels` component (PR #5). + +* Added `methods/logistic_regression` component (PR #5). + +* Added `metrics/accuracy` component (PR #5). ## MAJOR CHANGES +* Updated `api` files (PR #5). + +* Updated configs, components and CI to the latest Viash version (PR #8). + +## MINOR CHANGES +* Updated `README.md` (PR #5). +## BUGFIXES diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index 393f5ce2..aea76407 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -1,24 +1,21 @@ __merge__: ../../api/base_method.yaml -# A unique identifier for your component (required). -# Can contain only lowercase letters or underscores. + name: cellplm -# A relatively short label, used when rendering visualisations (required) + label: CellPLM -# A one sentence summary of how this method works (required). Used when -# rendering summary tables. + summary: "A foundation model pre-trained with cells as tokens." -# A multi-line description of how this component works (required). Used -# when rendering reference documentation. + description: | CellPLM is a pre-trained language model specifically designed for single-cell analysis that leverages the principles of natural language processing (NLP) to understand and process single-cell gene expression data. references: doi: - 10.1101/2023.10.03.560734 links: - # URL to the documentation for this method (required). + documentation: https://github.com/OmicsML/CellPLM/tree/main/tutorials - # URL to the code repository for this method (required). + repository: https://github.com/OmicsML/CellPLM @@ -32,10 +29,6 @@ arguments: description: String giving the CellPLM model to use choices: ["20231027_85M"] default: "20231027_85M" -# - name: --n_hvg -# type: integer -# default: 3000 -# description: Number of highly variable genes to use. resources: - type: python_script @@ -45,12 +38,11 @@ resources: engines: - type: docker image: openproblems/base_pytorch_nvidia:1.0.0 - # TODO: Try to find working installation of flash attention (flash-attn<1.0.5) setup: - type: python pypi: - gdown - - scgpt # Install from PyPI to get dependencies + - scgpt - cellplm runners: diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index 5feaef46..41b5a0ec 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -52,9 +52,6 @@ print(adata, flush=True) -print('Preprocess data', flush=True) -# ... preprocessing ... - print('Train model', flush=True) # ... train model ... From 6a98ccbfd249a28a890719c5ef89aa9ab47fa65c Mon Sep 17 00:00:00 2001 From: HelloWorldLTY Date: Wed, 8 Jan 2025 17:23:41 -0500 Subject: [PATCH 08/16] update correct label --- src/methods/cellplm/config.vsh.yaml | 4 ++-- src/methods/cellplm/script.py | 18 ++++++------------ 2 files changed, 8 insertions(+), 14 deletions(-) diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index aea76407..d8ba77b3 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -21,13 +21,13 @@ links: info: method_types: [embedding] - preferred_normalization: counts + preferred_normalization: log_cp10k arguments: - name: --model type: string description: String giving the CellPLM model to use - choices: ["20231027_85M"] + choices: ["20231027_85M", "20230926_85M"] default: "20231027_85M" resources: diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index 41b5a0ec..a5b27aff 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -52,17 +52,14 @@ print(adata, flush=True) -print('Train model', flush=True) -# ... train model ... - -drive_path = f"https://drive.google.com/drive/folders/1C2fVNEKX3plHnagaTwpuPW5tpwv1up9G?usp=sharing" -model_dir = tempfile.TemporaryDirectory() -print(f"Downloading from '{drive_path}'", flush=True) -gdown.download_folder(drive_path, output=model_dir.name, quiet=True) -print(f"Model directory: '{model_dir.name}'", flush=True) +model_dir = par['model'] +print(f"Model directory: '{model_dir}'", flush=True) pipeline = CellEmbeddingPipeline(pretrain_prefix=PRETRAIN_VERSION, # Specify the pretrain checkpoint to load - pretrain_directory=model_dir.name) + pretrain_directory=model_dir) + +print('Generate predictions', flush=True) +# ... generate predictions ... # DEVICE ='cpu' embedding = pipeline.predict(adata, # An AnnData object @@ -70,9 +67,6 @@ embedding = embedding.cpu().numpy() -print('Generate predictions', flush=True) -# ... generate predictions ... - output = ad.AnnData( obs=adata.obs[[]], var=adata.var[[]], From 28f3ce09dcbedbf3a094ef2bea4e4e65abb0471a Mon Sep 17 00:00:00 2001 From: Luke Zappia Date: Fri, 10 Jan 2025 08:33:23 +0100 Subject: [PATCH 09/16] Remove scgpt from cellplm config --- src/methods/cellplm/config.vsh.yaml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index d8ba77b3..1bc1d12f 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -8,9 +8,9 @@ label: CellPLM summary: "A foundation model pre-trained with cells as tokens." description: | - CellPLM is a pre-trained language model specifically designed for single-cell analysis that leverages the principles of natural language processing (NLP) to understand and process single-cell gene expression data. + CellPLM is a pre-trained language model specifically designed for single-cell analysis that leverages the principles of natural language processing (NLP) to understand and process single-cell gene expression data. references: - doi: + doi: - 10.1101/2023.10.03.560734 links: @@ -42,7 +42,6 @@ engines: - type: python pypi: - gdown - - scgpt - cellplm runners: From f35db1d75db1413e131b149841333eba9e11a926 Mon Sep 17 00:00:00 2001 From: Luke Zappia Date: Fri, 10 Jan 2025 09:16:29 +0100 Subject: [PATCH 10/16] Add model_name argument to cellplm Add code for extracting model files --- src/methods/cellplm/config.vsh.yaml | 7 +++++-- src/methods/cellplm/script.py | 32 ++++++++++++++++++++++++----- 2 files changed, 32 insertions(+), 7 deletions(-) diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index 1bc1d12f..b6c5add4 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -24,11 +24,15 @@ info: preferred_normalization: log_cp10k arguments: - - name: --model + - name: --model_name type: string description: String giving the CellPLM model to use choices: ["20231027_85M", "20230926_85M"] default: "20231027_85M" + - name: --model + type: file + description: Path to the directory containing CellPLM model files or a .zip/.tar.gz archive + required: true resources: - type: python_script @@ -41,7 +45,6 @@ engines: setup: - type: python pypi: - - gdown - cellplm runners: diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index a5b27aff..a63b1144 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -1,9 +1,10 @@ import sys import tempfile -import scanpy as sc import anndata as ad -import gdown import torch +import os +import zipfile +import tarfile import warnings warnings.filterwarnings("ignore") @@ -31,7 +32,6 @@ from read_anndata_partial import read_anndata set_seed(24) -PRETRAIN_VERSION = par['model'] DEVICE = "cuda" if torch.cuda.is_available() else "cpu" print("\n>>> Reading input files...", flush=True) @@ -52,10 +52,32 @@ print(adata, flush=True) -model_dir = par['model'] +print("\n>>> Getting model files...", flush=True) +# Available from https://www.dropbox.com/scl/fo/i5rmxgtqzg7iykt2e9uqm/h/ckpt?dl=0&subfolder_nav_tracking=1 +if os.path.isdir(par["model"]): + model_temp = None + model_dir = par["model"] +else: + model_temp = tempfile.TemporaryDirectory() + model_dir = model_temp.name + + if zipfile.is_zipfile(par["model"]): + print("Extracting CellPLM models from .zip...", flush=True) + with zipfile.ZipFile(par["model"], "r") as zip_file: + zip_file.extractall(model_dir) + elif tarfile.is_tarfile(par["model"]) and par["model"].endswith(".tar.gz"): + print("Extracting CellPLM models from .tar.gz...", flush=True) + with tarfile.open(par["model"], "r:gz") as tar_file: + tar_file.extractall(model_dir) + model_dir = os.path.join(model_dir, os.listdir(model_dir)[0]) + else: + raise ValueError( + f"The 'model' argument should be a directory a .zip file or a .tar.gz file" + ) + print(f"Model directory: '{model_dir}'", flush=True) -pipeline = CellEmbeddingPipeline(pretrain_prefix=PRETRAIN_VERSION, # Specify the pretrain checkpoint to load +pipeline = CellEmbeddingPipeline(pretrain_prefix=par["model_name"], pretrain_directory=model_dir) print('Generate predictions', flush=True) From b96e12c73dd673b42c15d171abad2c81dfd29093 Mon Sep 17 00:00:00 2001 From: Luke Zappia Date: Fri, 10 Jan 2025 09:42:22 +0000 Subject: [PATCH 11/16] Warn if GPU is not available for cellplm --- scripts/run_benchmark/run_full_local.sh | 2 +- scripts/run_benchmark/run_test_local.sh | 2 +- src/methods/cellplm/script.py | 9 +++++---- 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/scripts/run_benchmark/run_full_local.sh b/scripts/run_benchmark/run_full_local.sh index d823d79e..897edbc7 100755 --- a/scripts/run_benchmark/run_full_local.sh +++ b/scripts/run_benchmark/run_full_local.sh @@ -26,7 +26,7 @@ input_states: resources/datasets/**/state.yaml rename_keys: 'input_dataset:output_dataset;input_solution:output_solution' output_state: "state.yaml" publish_dir: "$publish_dir" -settings: '{"methods_exclude": ["uce"]}' +settings: '{"methods_exclude": ["uce", "cellplm"]}' HERE # run the benchmark diff --git a/scripts/run_benchmark/run_test_local.sh b/scripts/run_benchmark/run_test_local.sh index 2b72eeed..edfed812 100755 --- a/scripts/run_benchmark/run_test_local.sh +++ b/scripts/run_benchmark/run_test_local.sh @@ -21,7 +21,7 @@ input_states: resources_test/task_batch_integration/**/state.yaml rename_keys: 'input_dataset:output_dataset;input_solution:output_solution' output_state: "state.yaml" publish_dir: "$publish_dir" -settings: '{"methods_exclude": ["uce"]}' +settings: '{"methods_exclude": ["uce", "cellplm"]}' HERE nextflow run . \ diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index a63b1144..d3d5f09b 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -6,8 +6,6 @@ import zipfile import tarfile -import warnings -warnings.filterwarnings("ignore") from CellPLM.utils import set_seed import numpy as np @@ -32,7 +30,10 @@ from read_anndata_partial import read_anndata set_seed(24) -DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +device = "cuda" if torch.cuda.is_available() else "cpu" +if device == "cpu": + import warnings + warnings.warn("Loading CellPLM models requires a GPU, this run will fail") print("\n>>> Reading input files...", flush=True) print(f"Input H5AD file: '{par['input']}'", flush=True) @@ -85,7 +86,7 @@ # DEVICE ='cpu' embedding = pipeline.predict(adata, # An AnnData object - device=DEVICE) # Specify a gpu or cpu for model inference + device=device) # Specify a gpu or cpu for model inference embedding = embedding.cpu().numpy() From e6a15cdd85198ac70fdde44377aaeff962d717c0 Mon Sep 17 00:00:00 2001 From: Luke Zappia Date: Fri, 10 Jan 2025 09:46:13 +0000 Subject: [PATCH 12/16] Fix model temp dir cleanup --- src/methods/cellplm/script.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index d3d5f09b..d3611e68 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -106,5 +106,8 @@ output.write_h5ad(par['output'], compression='gzip') -print("\n>>> Cleaning up temporary directories...", flush=True) -model_dir.cleanup() +if model_temp is not None: + print("\n>>> Cleaning up temporary directories...", flush=True) + model_temp.cleanup() + +print("\n>>> Done!", flush=True) From 4454035f65fc13155f85b109a3fb7a32491ebfe9 Mon Sep 17 00:00:00 2001 From: Luke Zappia Date: Fri, 10 Jan 2025 10:22:00 +0000 Subject: [PATCH 13/16] Add cellplm to benchmark workflow --- src/workflows/run_benchmark/config.vsh.yaml | 1 + src/workflows/run_benchmark/main.nf | 3 +++ 2 files changed, 4 insertions(+) diff --git a/src/workflows/run_benchmark/config.vsh.yaml b/src/workflows/run_benchmark/config.vsh.yaml index 75c93003..5239d544 100644 --- a/src/workflows/run_benchmark/config.vsh.yaml +++ b/src/workflows/run_benchmark/config.vsh.yaml @@ -84,6 +84,7 @@ dependencies: - name: methods/batchelor_fastmnn - name: methods/batchelor_mnn_correct - name: methods/bbknn + - name: methods/cellplm - name: methods/combat - name: methods/geneformer - name: methods/harmony diff --git a/src/workflows/run_benchmark/main.nf b/src/workflows/run_benchmark/main.nf index afcb968c..dcc5bc5d 100644 --- a/src/workflows/run_benchmark/main.nf +++ b/src/workflows/run_benchmark/main.nf @@ -19,6 +19,9 @@ methods = [ batchelor_fastmnn, batchelor_mnn_correct, bbknn, + cellplm.run( + args: [model: file("s3://openproblems-work/cache/cellplm-ckpt.zip")] + ), combat, geneformer, harmony, From 3951a7de887d283873e1ec44b97b9b1aec7095ba Mon Sep 17 00:00:00 2001 From: Luke Zappia Date: Fri, 10 Jan 2025 11:38:18 +0100 Subject: [PATCH 14/16] Tidy and style cellplm script --- src/methods/cellplm/script.py | 50 +++++++++++++++-------------------- 1 file changed, 21 insertions(+), 29 deletions(-) diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index d3611e68..1dcc4b2c 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -1,29 +1,24 @@ +import os import sys +import tarfile import tempfile -import anndata as ad -import torch -import os import zipfile -import tarfile - -from CellPLM.utils import set_seed -import numpy as np import anndata as ad +import torch from CellPLM.pipeline.cell_embedding import CellEmbeddingPipeline +from CellPLM.utils import set_seed ## VIASH START # Note: this section is auto-generated by viash at runtime. To edit it, make changes # in config.vsh.yaml and then run `viash config inject config.vsh.yaml`. par = { - 'input': 'resources_test/.../input.h5ad', - 'output': 'output.h5ad', - "model": "20231027_85M", -} -meta = { - 'name': 'cellplm' + "input": "resources_test/.../input.h5ad", + "output": "output.h5ad", + "model": "20231027_85M", } +meta = {"name": "cellplm"} ## VIASH END sys.path.append(meta["resources_dir"]) @@ -33,16 +28,13 @@ device = "cuda" if torch.cuda.is_available() else "cpu" if device == "cpu": import warnings + warnings.warn("Loading CellPLM models requires a GPU, this run will fail") print("\n>>> Reading input files...", flush=True) print(f"Input H5AD file: '{par['input']}'", flush=True) adata = read_anndata( - par['input'], - X='layers/normalized', - obs='obs', - var='var', - uns='uns' + par["input"], X="layers/normalized", obs="obs", var="var", uns="uns" ) if adata.uns["dataset_organism"] != "homo_sapiens": @@ -73,23 +65,21 @@ model_dir = os.path.join(model_dir, os.listdir(model_dir)[0]) else: raise ValueError( - f"The 'model' argument should be a directory a .zip file or a .tar.gz file" + "The 'model' argument should be a directory a .zip file or a .tar.gz file" ) print(f"Model directory: '{model_dir}'", flush=True) -pipeline = CellEmbeddingPipeline(pretrain_prefix=par["model_name"], - pretrain_directory=model_dir) - -print('Generate predictions', flush=True) -# ... generate predictions ... - -# DEVICE ='cpu' -embedding = pipeline.predict(adata, # An AnnData object - device=device) # Specify a gpu or cpu for model inference +print("\n>>> Creating embedding model pipeline...", flush=True) +pipeline = CellEmbeddingPipeline( + pretrain_prefix=par["model_name"], pretrain_directory=model_dir +) +print("\n>>> Embedding data...", flush=True) +embedding = pipeline.predict(adata, device=device) embedding = embedding.cpu().numpy() +print("\n>>> Storing output...", flush=True) output = ad.AnnData( obs=adata.obs[[]], var=adata.var[[]], @@ -104,7 +94,9 @@ ) print(output) -output.write_h5ad(par['output'], compression='gzip') +print("\n>>> Writing output to file...", flush=True) +print(f"Output H5AD file: '{par['output']}'", flush=True) +output.write_h5ad(par["output"], compression="gzip") if model_temp is not None: print("\n>>> Cleaning up temporary directories...", flush=True) From 7b4f4a943ed93beacd2d5360448c715ed68889b7 Mon Sep 17 00:00:00 2001 From: Luke Zappia Date: Mon, 27 Jan 2025 16:12:36 +0100 Subject: [PATCH 15/16] Adjust cellplm to use counts --- src/methods/cellplm/config.vsh.yaml | 6 +----- src/methods/cellplm/script.py | 2 +- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index b6c5add4..a8d700c0 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -1,6 +1,5 @@ __merge__: ../../api/base_method.yaml - name: cellplm label: CellPLM @@ -13,15 +12,12 @@ references: doi: - 10.1101/2023.10.03.560734 links: - documentation: https://github.com/OmicsML/CellPLM/tree/main/tutorials - repository: https://github.com/OmicsML/CellPLM - info: method_types: [embedding] - preferred_normalization: log_cp10k + preferred_normalization: counts arguments: - name: --model_name diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index 1dcc4b2c..714de5d5 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -34,7 +34,7 @@ print("\n>>> Reading input files...", flush=True) print(f"Input H5AD file: '{par['input']}'", flush=True) adata = read_anndata( - par["input"], X="layers/normalized", obs="obs", var="var", uns="uns" + par["input"], X="layers/counts", obs="obs", var="var", uns="uns" ) if adata.uns["dataset_organism"] != "homo_sapiens": From 938d673f49ca17ab8f506b04001059099615b8b3 Mon Sep 17 00:00:00 2001 From: Luke Zappia Date: Mon, 27 Jan 2025 16:42:32 +0100 Subject: [PATCH 16/16] Add non-applicable exit code to CellPLM --- src/methods/cellplm/config.vsh.yaml | 1 + src/methods/cellplm/script.py | 9 ++++----- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/methods/cellplm/config.vsh.yaml b/src/methods/cellplm/config.vsh.yaml index a8d700c0..df8b9e71 100755 --- a/src/methods/cellplm/config.vsh.yaml +++ b/src/methods/cellplm/config.vsh.yaml @@ -34,6 +34,7 @@ resources: - type: python_script path: script.py - path: /src/utils/read_anndata_partial.py + - path: /src/utils/exit_codes.py engines: - type: docker diff --git a/src/methods/cellplm/script.py b/src/methods/cellplm/script.py index 714de5d5..6e6a20c8 100644 --- a/src/methods/cellplm/script.py +++ b/src/methods/cellplm/script.py @@ -22,6 +22,7 @@ ## VIASH END sys.path.append(meta["resources_dir"]) +from exit_codes import exit_non_applicable from read_anndata_partial import read_anndata set_seed(24) @@ -33,14 +34,12 @@ print("\n>>> Reading input files...", flush=True) print(f"Input H5AD file: '{par['input']}'", flush=True) -adata = read_anndata( - par["input"], X="layers/counts", obs="obs", var="var", uns="uns" -) +adata = read_anndata(par["input"], X="layers/counts", obs="obs", var="var", uns="uns") if adata.uns["dataset_organism"] != "homo_sapiens": - raise ValueError( + exit_non_applicable( f"CellPLM can only be used with human data " - f"(dataset_organism == \"{adata.uns['dataset_organism']}\")" + f'(dataset_organism == "{adata.uns["dataset_organism"]}")' ) print(adata, flush=True)