API

Import CAT as:

import cat

cat

internal_preprocessing(ds1, ds2[, ...])	The internal preprocessing of the CAT algorithm ensures that data is in the right shape and format.
compare(ds1, ds2[, n_iterations, features, ...])	CAT routine calculated the inter cluster distances.
run(args)	Run CAT inference

Dataset and DatasetDiff

Dataset._fix_metadata(group_by)	Helper function to fix metadata information and add cluster_field to the obs
Dataset._fix_genes(gene_symbol_field)	Set correct column from var to gene symbol.
Dataset._fix_genes(gene_symbol_field)	Set correct column from var to gene symbol.
Dataset._filter_genes(gene_type, pattern)	Filter genes based on pattern
Dataset._save(save_path)	Save dataset into anndata format
Dataset.prepare(group_by[, ...])	Prepares dataset for CAT analysis
DatasetDiff(ds1_name, ds2_name, mean, std)	Pairwise dataset container

Report

report.generate_tables(diff, sigma_th)	Takes the raw results from the CAT routine function and turns them into nice table per cluster.
report.to_excel(args, tables)	Generate CAT results to Excel format
report.save_tables(args, tables)	Save results into multiple formats

Utils

utils.get_nz_mean(mat)	Calculate non-zero mean
utils.get_nz_median(mat)	Calculate non-zero median
utils.normalize(mat[, method])	Normalize count matrix
utils.rename_ds(names)	Rename dataset names
utils.read_features(file)	Read feature file containing list in TSV format (tab separated).