Deep probabilistic analysis of single-cell and spatial omics data

muon is a multimodal omics Python framework

scPerturb: A resource and a python/R tool for single-cell perturbation data

Single cell analysis in the browser

iCellR is an interactive R package designed to facilitate the analysis and visualization of high-throughput single-cell sequencing data. It supports a variety of single-cell technologies, including scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq, and Spatial Transcriptomics (ST).

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).

Normalize CITEseq Data

scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics

A Snakemake workflow and MrBiomics module for processing and visualizing (multimodal) sc/snRNA-seq data generated with 10X Genomics Kits or in the MTX matrix file format powered by the R package Seurat.

ADTnorm normalizes the cell surface protein measurement of CITE-seq data, facilitating across batches and across studies data integration.

scpca-nf is the Nextflow workflow for processing Single-cell Pediatric Cancer Atlas Portal data

MultiModal Classifier Hierarchy (MMoCHi)

Code and results from TotalSeqC antibody titration and pipeline benchmarking for CITE-seq experiments

Novel joint clustering method with scRNA-seq and CITE-seq data

experimental interface between R and scvi-tools

CITE-seq profiling of human PBMCs at baseline and activation conditions

Tools for single-cell feature barcoding analysis

Scripts related to bulkRNA-seq as well as single-cell CITE-seq and VDJ-seq data of Gearty et al. (2021)

Project analyzing multi-omics genomics data from a COVID-19 study.