Preprocessing

preprocessing.dendrogram(adata, cluster_header, *, plot=False, save=False, figsize=(12, 2), output_folder='', outputfilename_suffix='', **kwargs)

Generating a dendrogram from the AnnData object.

Parameters

adata: AnnData

Annotated data matrix.

cluster_header: str

Column in adata.obs storing cell annotation. Passed into scanpy’s dendrogram as groupby.

plot: bool (default: False)

Whether to use sc.pl.dendrogram instead of sc.tl.dendrogram.

save: bool | str (default: False)

Whether to save plot in output_folder. If string, choose the type of file to save as (‘png’(default), ‘svg’, ‘pdf).

figsize: tuple (default: (12, 2))

figure.figsize for plt.rc_context.

output_folder: str (default: “”)

Output folder. Created if doesn’t exist.

outputfilename_suffix: str (default: “”)

Suffix for all output files.

kwargs: dictionary (default: None)

Additional parameters to pass to sc.tl.dendrogram or sc.pl.dendrogram.

Returns

does not return anything. Adds adata.uns[“dendrogram_{cluster_header}”] to passed in adata.

preprocessing.prep_medians(adata, cluster_header, use_mean=False, positive_genes_only=True)

Calculating the median expression matrix. Subsetting adata if positive_genes_only = True.

Parameters

adata: AnnData

Annotated data matrix.

cluster_header: str

Column in adata.obs storing cell annotation.

use_mean: bool (default: False)

Whether to use the mean (vs median) for minimum gene expression threshold.

positive_genes_only: bool (default: True)

Whether to subset AnnData to only have genes with median/mean expression greater than 0.

Returns

adata: AnnData

AnnData with median expression values stored in adata.varm[“medians_{cluster_header}”].

preprocessing.get_medians(adata, cluster_header, use_mean=False)

Calculating the median (mean) expression per gene for each cluster_header.

Parameters

adata: AnnData

Annotated data matrix.

cluster_header: str

Column in adata.obs storing cell annotation.

use_mean: bool (default: False)

Whether to use the mean (vs median) for minimum gene expression threshold.

Returns

cluster_medians: pd.DataFrame

Gene-by-cluster median (mean) expression dataframe.

preprocessing.prep_binary_scores(adata, cluster_header, medians_header='medians_')

Calculating the binary scores of each gene per cluster_header.

Parameters

adata: AnnData

Annotated data matrix.

cluster_header: str

Column in adata.obs storing cell annotation.

medians_header: str (default: “medians_{cluster_header}”)

Key in adata.varm storing median expression matrix.

Returns

adata: AnnData

AnnData with binary scores stored in adata.varm[“binary_scores_{cluster_header}”].