Documentation

Import scDRS as:

import scdrs

Compute scDRS score

scdrs.preprocess(data[, cov, adj_prop, ...])	Preprocess single-cell data for scDRS analysis.
scdrs.score_cell(data, gene_list[, ...])	Score cells based on the disease gene set.

scDRS downstream analyses

scdrs.method.downstream_group_analysis(...)	scDRS group-level analysis.
scdrs.method.downstream_corr_analysis(adata, ...)	scDRS cell-level correlation analysis.
scdrs.method.downstream_gene_analysis(adata, ...)	scDRS gene-level correlation analysis.

Data loading

scdrs.util.load_h5ad(h5ad_file[, ...])	Load h5ad file and optionally filter out cells and perform normalization.
scdrs.util.load_scdrs_score(score_file[, ...])	Load scDRS scores.
scdrs.util.load_gs(gs_path[, src_species, ...])	Load the gene set file (.gs file).
scdrs.util.load_homolog_mapping(src_species, ...)	Load gene homologs between mouse and human.

Utils

scdrs.method.test_gearysc(adata, ...[, opt])	Compute significance level for Geary's C statistics.
scdrs.method.gearys_c(adata, vals)	Compute Geary's C statistics for an AnnData.
scdrs.method._pearson_corr(mat_X, mat_Y)	Pearson's correlation between every columns in mat_X and mat_Y.
scdrs.method._pearson_corr_sparse(mat_X, mat_Y)	Pearson's correlation between every columns in mat_X and mat_Y (sparse matrix)
scdrs.pp.compute_stats(adata[, ...])	Compute gene-level and cell-level statstics used for scDRS analysis.
scdrs.pp.reg_out(mat_Y, mat_X)	Regress mat_X out of mat_Y.
scdrs.pp._get_mean_var(sparse_X[, axis, weights])	Compute mean and var of a sparse / non-sparse matrix.
scdrs.pp._get_mean_var_implicit_cov_corr(adata)	Compute mean and variance of sparse matrix of the form