Tools: tl

The tools module dotools_py.tl contains functions for the downstream analysis and statistical methods.

Downstream processing

tl.integrate_data(adata, batch_key[, ...])	Integrate a concatenated AnnData.
tl.run_scvi(adata, batch_key[, gene_key, ...])	Run scVI.
tl.run_scanvi(adata, batch_key, label_key[, ...])	Run scANVI.
tl.run_harmony(adata, batch_key[, use_rep, ...])	Run Harmony integration.
tl.run_seurat_integration(adata, batch_key)	Run Seurat Integration methods.
tl.auto_annot(adata, cluster_key[, model, ...])	Semi-automatic annotation based on CellTypist.
tl.reclustering(adata, cluster_key, ...[, ...])	Re-clustering of dataset.
tl.full_recluster(adata, cluster_key, ...[, ...])	Re-clustering of all clusters in dataset.
tl.go_analysis(df, gene_key, pval_key, ...)	Run Gene Ontology using EnrichR API.

Statistical Analysis

tl.DGEAnalysis(adata, groupby[, batch_key, ...])	Class to perform differential gene expression (DGE) at the single-cell or sample level for AnnData objects.
tl.rank_genes_groups(adata, groupby, *[, ...])	Rank genes for characterizing groups.
tl.rank_genes_condition(adata, groupby[, ...])	Run DGE Analysis.
tl.rank_genes_pseudobulk(adata, ctrl_cond, ...)	Running DEA using pseudobulk approach.
tl.rank_genes_consensus(adata, ctrl_cond, ...)	Run single-cell and pseudo-bulk differential expression analysis.
tl.grouped_ttest(adata[, annot_key, ...])	Calculate grouped t-test.
tl.run_mast(adata, cond_key, reference, disease)	Run MAST Test for sc/snRNAseq.