API

Import decoupler as:

import decoupler as dc

Preprocessing:

extract(mat[, use_raw, verbose, dtype])	Processes different input types so that they can be used downstream.
filt_min_n(c, net[, min_n])	Removes sources of a net with less than min_n targets.
match(c, r, net)	Matches mat with a regulatory adjacency matrix.
rename_net(net[, source, target, weight])	Renames input network to match decoupleR's format (source, target, weight).
get_net_mat(net)	Transforms a given network to a regulatory adjacency matrix (targets x sources).

Methods:

run_aucell(mat, net[, source, target, n_up, ...])	AUCell.
run_gsea(mat, net[, source, target, times, ...])	Gene Set Enrichment Analysis (GSEA).
run_gsva(mat, net[, source, target, kcdf, ...])	Gene Set Variation Analysis (GSVA).
run_mlm(mat, net[, source, target, weight, ...])	Multivariate Linear Model (MLM).
run_ora(mat, net[, source, target, n_up, ...])	Over Representation Analysis (ORA).
run_ulm(mat, net[, source, target, weight, ...])	Univariate Linear Model (ULM).
run_viper(mat, net[, source, target, ...])	Virtual Inference of Protein-activity by Enriched Regulon (VIPER).
run_wmean(mat, net[, source, target, ...])	Weighted sum (WMEAN).
run_wsum(mat, net[, source, target, weight, ...])	Weighted sum (WSUM).
run_consensus(res)	Consensus.

Running multiple methods:

decouple(mat, net[, source, target, weight, ...])

Decouple function.

Utils:

show_methods()	Shows available methods.
check_corr(net[, source, target, weight, ...])	Checks the correlation across the regulators in a network.
melt(df)	Function to generate a long format dataframe similar to the one obtained in the R implementation of decoupleR.
get_acts(adata, obsm_key)	Extracts activities as AnnData object.
get_toy_data([n_samples, seed])	Generate a toy mat and net for testing.
summarize_acts(acts, groupby[, obs, var, ...])	Summarizes activities obtained per group by their mean or median.
assign_groups(summary)	Assigns group labels based on summary activities.
get_pseudobulk(adata, sample_col, groups_col)	Generates an unormalized pseudo-bulk profile per sample and group.
get_contrast(adata, group_col, ...[, method])	Computes simple contrast between two conditions from pseudo-bulk profiles.

Omnipath wrappers:

show_resources()	Shows available resources in Omnipath.
get_resource(name)	Wrapper to access resources inside Omnipath.
get_progeny([organism, top])	Pathway RespOnsive GENes for activity inference (PROGENy).
get_dorothea([organism, levels, weight_dict])	DoRothEA gene regulatory network.

Plotting

plot_volcano(logFCs, pvals, name, contrast, net)

Plot logFC and p-values of a selected source by a specific contrast.