API#

Import pairOT as:

import pairot as pr

Preprocessing#

`pp.preprocess_adatas`(adata1, adata2, ...[, ...])	Function for pre-processing the input AnnData objects for usage with `pairot.tl.DatasetMap`.
`pp.downsample_indices`(labels, n_samples[, ...])	Downsample indices stratified by labels.
`pp.rank_genes_limma`(adata, cluster_label, ...)	Calculate pseudobulk differential expression (DE) statistics using limmaR package.
`pp.filter_genes_ova`(de_res[, ...])	Sort and filter DEGs for the OVA (one vs all) setting.
`pp.filter_genes_ava`(de_res[, ...])	Sort and filter DEGs for the AVA (all vs all) setting.
`pp.select_genes`(de_res_ova, de_res_ava[, ...])	Select and combine DE results from OVA (one vs all) and AVA (all vs all) settings.

tl.DatasetMap(adata1, adata2)

Align cell annotations between query and reference dataset using annotation-informed optimal transport.

`pl.mapping`(data[, width, height, zmin, ...])	Plot cluster mappings from `pairot.tl.DatasetMap.compute_mapping`.
`pl.distance`(data[, width, height, backend])	Plot cluster distances from `pairot.tl.DatasetMap.compute_distance`.
`pl.sankey`(cluster_mapping, cluster_distance)	Plot cluster mappings and distances from `pairot.tl.DatasetMap` as a Sankey diagram.

`pp.OFFICIAL_GENES`	DataFrame containing official gene names from genenames.org.
`pp.FILTERED_GENES`	DataFrame containing uninformative genes to filter out, e.g., mitochondrial, ribosomal, IncRNA, TCR and BCR genes.