Skip to content

Removed plot parameter settings #4

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
SabrinaRichter wants to merge 3 commits into
base: master
Choose a base branch
from
Open

Removed plot parameter settings #4

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
a5d6bc1
removed stuff that is messing with my plots
SabrinaRichter Oct 31, 2022
9e82dba
also imputing nan values
SabrinaRichter Oct 31, 2022
a2d4f9a
allowed for non unique FileID + Channel combinations
SabrinaRichter Nov 2, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
59 changes: 18 additions & 41 deletions sceptre/sceptre.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,34 +15,6 @@
import seaborn as sns
import warnings

# output scanpy logs
logging.basicConfig(level=logging.INFO)
sc.settings.verbosity = 3
# don´t show this numpy warning
warnings.filterwarnings("ignore", message="All-NaN slice encountered")

# basic plotting settings
plt.rcParams["xtick.labelsize"] = 8
plt.rcParams["ytick.labelsize"] = 8
plt.rcParams["font.size"] = 8
plt.rcParams["font.family"] = "sans-serif"
plt.rcParams["font.sans-serif"] = "Arial"
plt.rcParams["figure.figsize"] = 7.2, 4.45
plt.rcParams["figure.titlesize"] = 9
plt.rcParams["figure.dpi"] = 120
plt.rcParams["axes.titlesize"] = 9
plt.rcParams["axes.labelsize"] = 8
plt.rcParams["axes.axisbelow"] = True
plt.rcParams["axes.linewidth"] = 0.5
plt.rcParams["lines.linewidth"] = 0.7
plt.rcParams["lines.markersize"] = 2
plt.rcParams["legend.fontsize"] = 8
plt.rcParams["boxplot.flierprops.marker"] = "."
plt.rcParams["boxplot.flierprops.markerfacecolor"] = "k"
plt.rcParams["boxplot.flierprops.markersize"] = 2
plt.rcParams["pdf.fonttype"] = 42 # to make pdf text available for illustrator
plt.rcParams["ps.fonttype"] = 42 # to make pdf text available for illustrator

figwd = 7.2 # standard figure width
cellsize = 20 # size to plot cells
wspace = 1 # space between scanpy plots to make room for legends
Expand Down Expand Up @@ -568,7 +540,8 @@ def normalize(
quant = pd.DataFrame(
adata.X.T.copy(),
columns=adata.obs[["File ID", "Channel"]]
.set_index(["File ID", "Channel"])
.reset_index()
.set_index(["index", "File ID", "Channel"])
.index,
).replace(0, np.nan)

Expand All @@ -582,7 +555,7 @@ def normalize(
med_tot = med.median(axis=1)
factors = med.divide(med_tot, axis=0)

quant = quant.groupby(axis=1, level=0).apply(
quant = quant.groupby(axis=1, level="File ID", group_keys=False).apply(
lambda x: x.divide(factors.loc[:, x.name], axis=0)
)

Expand All @@ -593,7 +566,7 @@ def normalize(
med_tot = med.median(axis=1)
factors = med.divide(med_tot, axis=0)

quant = quant.groupby(axis=1, level=1).apply(
quant = quant.groupby(axis=1, level="Channel", group_keys=False).apply(
lambda x: x.divide(factors.loc[:, x.name], axis=0)
)

Expand All @@ -611,7 +584,7 @@ def normalize(
med_tot = med.median(axis=1)
factors = med.divide(med_tot, axis=0)

quant = quant.groupby(axis=1, level=0).apply(
quant = quant.groupby(axis=1, level="File ID", group_keys=False).apply(
lambda x: x.divide(factors.loc[:, x.name], axis=0)
)

Expand All @@ -624,7 +597,7 @@ def normalize(
med_tot = med.median(axis=1)
factors = med.divide(med_tot, axis=0)

quant = quant.groupby(axis=1, level=1).apply(
quant = quant.groupby(axis=1, level="Channel", group_keys=False).apply(
lambda x: x.divide(factors.loc[:, x.name], axis=0)
)

Expand Down Expand Up @@ -1091,16 +1064,20 @@ def impute(adata: AnnData, **kwargs):

Updates `adata` with the imputed expression values.
"""
if 0 in adata.X:
from sklearn.impute import KNNImputer

imputer = KNNImputer(missing_values=0, **kwargs)
if (adata.X == 0).all(axis=0).any():
print("remove all-zero proteins:")
sc.pp.filter_genes(adata, min_cells=1)
adata.X = imputer.fit_transform(adata.X)
if np.isnan(adata.X).any():
missing_values = np.nan
elif 0 in adata.X:
missing_values = 0
else:
warnings.warn("No zeros in adata")
return

from sklearn.impute import KNNImputer
imputer = KNNImputer(missing_values=missing_values, **kwargs)
sc.pp.filter_genes(adata, min_cells=1)
adata.X = imputer.fit_transform(adata.X)




def find_embedding_params(
Expand Down