sc-de

name	sc-de
description	Differential expression for single-cell RNA-seq using exploratory Scanpy ranking, R-backed MAST, or replicate-aware pseudobulk DESeq2. The wrapper separates cluster/group marker ranking from sample-aware condition DE.
version	0.6.0
author	OmicsClaw
license	MIT
tags	["singlecell","differential-expression","markers","wilcoxon","deseq2"]
metadata	{"omicsclaw":{"domain":"singlecell","allowed_extra_flags":["--celltype-key","--group1","--group2","--groupby","--log2fc-threshold","--logreg-solver","--method","--n-top-genes","--padj-threshold","--pseudobulk-min-cells","--pseudobulk-min-counts","--sample-key","--r-enhanced"],"param_hints":{"wilcoxon":"[Truncated]","t-test":"[Truncated]","logreg":"[Truncated]","mast":"[Truncated]","deseq2_r":"[Truncated]"},"saves_h5ad":true,"requires_preprocessed":true,"requires":{"bins":"[Truncated]","env":"[Truncated]","config":"[Truncated]"},"emoji":"S","homepage":"https://github.com/OmicsClaw/OmicsClaw","os":["macos","linux"],"install":["[Truncated]"],"trigger_keywords":["differential expression","marker genes","de analysis","wilcoxon","pseudo-bulk"]}}

Single-Cell Differential Expression

Why This Exists

• Without it: users mix exploratory marker tests with replicate-aware inference and misread the results.
• With it: the wrapper makes the statistical path explicit and preserves the public DE contract.
• Why OmicsClaw: one interface covers quick Scanpy ranking and the heavier pseudobulk route.

Core Capabilities

1. Five DE paths: Wilcoxon, t-test, logistic regression, MAST, and DESeq2 pseudobulk.
2. Separation of statistical questions: exploratory single-cell ranking versus replicate-aware pseudobulk inference.
3. Matrix-aware contract: normalized expression for ranking paths, raw counts for DESeq2 pseudobulk.
4. Direct DE figure exports: marker dotplot and rank-gene summary where supported.
5. Downstream-ready export: processed AnnData, DE tables, report, structured result JSON, README, and notebook artifacts.

Scope Boundary

Implemented methods:

1. wilcoxon
2. t-test
3. logreg
4. mast for R-backed hurdle-model DE
5. deseq2_r for pseudobulk DE

Input Formats

Format	Extension / form	Current wrapper support	Notes
AnnData	.h5ad	yes	current direct input path
Demo	--demo	yes	bundled fallback

Input Expectations

• Accepted input: processed .h5ad
• wilcoxon, t-test, logreg, and mast expect X = normalized_expression
• deseq2_r expects raw counts, preferably in layers["counts"], otherwise aligned raw counts in adata.raw, otherwise count-like adata.X
• All methods require a grouping column in obs
• If you want cluster markers, this skill usually comes after