sc-filter

name	sc-filter
description	Filter cells and genes from single-cell RNA-seq AnnData objects using QC-derived thresholds or tissue presets. This wrapper removes low-quality cells/genes but does not normalize, cluster, or annotate the dataset.
version	0.4.0
author	OmicsClaw
license	MIT
tags	["singlecell","filter","qc","preprocessing"]
metadata	{"omicsclaw":{"domain":"singlecell","allowed_extra_flags":["--max-counts","--max-genes","--max-mt-percent","--min-cells","--min-counts","--min-genes","--tissue","--r-enhanced"],"param_hints":{"threshold_filtering":"[Truncated]"},"saves_h5ad":true,"requires_preprocessed":false,"requires":{"bins":"[Truncated]","env":"[Truncated]","config":"[Truncated]"},"emoji":"S","homepage":"https://github.com/OmicsClaw/OmicsClaw","os":["macos","linux"],"install":[],"trigger_keywords":["filter cells","cell filtering","gene filtering","remove low quality","qc filtering","tissue-specific thresholds"]}}

Single-Cell Filter

Why This Exists

• Without it: downstream clustering and DE are distorted by low-quality droplets and low-information genes.
• With it: one run applies explicit QC cutoffs and records how many cells/genes were removed.
• Why OmicsClaw: tissue presets and a stable output contract make filtering easier to reproduce.

Core Capabilities

1. Threshold-based filtering: gene-count, count-depth, and mitochondrial cutoffs in one wrapper.
2. Tissue-aware presets: wrapper-level heuristics for common tissues.
3. QC-aware operation: reuses existing QC metrics or computes the minimum needed metrics first.
4. Standard gallery contract: before/after QC comparison and retention summary under figures/.
5. Downstream-ready export: processed.h5ad, filter-stat tables, result JSON, README, and notebook artifacts.

Scope Boundary

This skill currently exposes one public workflow: threshold_filtering.

This skill does:

1. use existing QC metrics or compute the ones needed for thresholding
2. filter cells by genes, counts, and mitochondrial percentage
3. filter genes by minimum detected-cell count
4. export filtered AnnData, figures, tables, and a report

This skill does not:

1. normalize counts
2. run HVG, PCA, UMAP, or clustering
3. remove doublets or ambient RNA

Input Formats

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

Input Expectations

• Accepted input: .h5ad
• Expected data state: either count-oriented AnnData or QC-annotated AnnData
• Important columns when present: n_genes_by_counts, total_counts, pct_counts_mt
• If QC metrics are missing but raw counts are still recoverable, the wrapper canonicalizes the object and computes the minimum needed QC metrics automatically