scatrans.differential_expression

scatrans.differential_expression#

scatrans.differential_expression(adata_input, groupby='condition', target_group=None, reference_group=None, subset_col=None, subset_values=None, de_method='t-test_overestim_var', pseudobulk_de_backend='pydeseq2', pydeseq2_min_counts=10, use_pseudobulk=False, sample_col=None, min_cells=10, min_counts=1000, pb_x_layer='X', pb_use_total_for_x=True, de_preprocess='auto', min_total_counts=50, strict_pydeseq2_counts=True, use_mixed_model=False, use_delta_variance_pval=False, delta_var_pval_cutoff=0.05, mixed_model_pval='wald', paired_replicates=False, use_memento_de=False, memento_capture_rate=0.07, memento_num_boot=5000, memento_n_cpus=-1, n_jobs=-1, gene_type_filter=None, counts=None, copy_input=True)[source]#

Standalone differential expression (DE) using the same flexible backends as scATrans (scanpy methods, PyDESeq2 pseudobulk, mixed linear models, and Memento – the Cell 2024 method-of-moments framework).

This function does not require spliced/unspliced (velocity) layers. It is intended for users who want high-quality DE (especially via Memento), followed by scATrans’ downstream tools:

candidates = scat.filter_active_genes(de_results, pval_cutoff=0.05, logfc_cutoff=0.3) # upregulated # down or both directions: # down_cands = scat.filter_active_genes(de_results, pval_cutoff=0.05, logfc_cutoff=0.3, logfc_direction=”down”) # For enrichment, pass adata= (if store_raw_counts was used) so it uses # the preserved full measured gene set as universe, not just current HVGs. enrich = scat.run_enrichment(candidates.index.tolist(), …, adata=adata) scat.pl.volcano_plot(de_results, …) scat.pl.enrich_dotplot(enrich, …)

All DE-related options from active_score are supported here (pseudobulk, mixed models, Memento, etc.), except permutation-based FDR (use active_score(..., use_permutation=True) when velocity layers are available). For a minimal-parameter entry point see active_score_simple or run_default_pipeline.

copy_inputbool, default True

Same semantics as active_score(): one combined obs-filter copy when True; zero AnnData.copy() calls when False and no obs filtering is needed.

Returns:

  • results_df is sorted by p_adj (ascending; most significant first) and contains at minimum: logFC, p_val, p_adj, and (when use_memento_de) the native memento_de_* / memento_dv_* columns.

  • adata.var is updated with the same columns for convenience.

  • Metadata is stored under adata.uns[“scatrans”].

Return type:

(adata_with_results, results_df)

Parameters:

  • adata_input (Any)

  • groupby (str)

  • target_group (str | None)

  • reference_group (str | None)

  • subset_col (str | None)

  • subset_values (str | list[str] | tuple[str, ...] | None)

  • de_method (str)

  • pseudobulk_de_backend (str)

  • pydeseq2_min_counts (int)

  • use_pseudobulk (bool)

  • sample_col (str | None)

  • min_cells (int)

  • min_counts (int)

  • pb_x_layer (str)

  • pb_use_total_for_x (bool)

  • de_preprocess (str)

  • min_total_counts (int)

  • strict_pydeseq2_counts (bool)

  • use_mixed_model (bool)

  • use_delta_variance_pval (bool)

  • delta_var_pval_cutoff (float)

  • mixed_model_pval (str)

  • paired_replicates (bool)

  • use_memento_de (bool)

  • memento_capture_rate (float)

  • memento_num_boot (int)

  • memento_n_cpus (int)

  • n_jobs (int)

  • gene_type_filter (str | None)

  • counts (str | ndarray | spmatrix | DataFrame | AnnData | None)

  • copy_input (bool)