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bio-single-cell-scatac-analysis
Maintainer FreedomIntelligence · Last updated April 1, 2026
Single-cell ATAC-seq analysis with Signac (R/Seurat) and ArchR. Process 10X Genomics scATAC data, perform QC, dimensionality reduction, clustering, peak calling, and motif activity scoring with chromVAR. Use when analyzing single-cell ATAC-seq data.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-single-cell-scatac-analysis
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- R: Signac::CreateChromatinAssay() → RunTFIDF() → FindTopFeatures() → RunSVD().
- R: ArchR::createArrowFiles() for large datasets.
- Analyze my single-cell ATAC-seq data" → Process peak-barcode matrices, perform QC/filtering, reduce dimensions with LSI, cluster cells, call peaks per cluster, and score motif activity. R: Signac::CreateChromatinAssay() → RunTFIDF() → FindTopFeatures() → RunSVD() R: ArchR::createArrowFiles() for large datasets.
- Analyze single-cell chromatin accessibility data to identify cell types and regulatory elements.
- peak_counts <- FeatureMatrix(fragments = Fragments(obj), features = peaks, cells = colnames(obj)).
Source Doc
Excerpt From SKILL.md
Tool Comparison
| Tool | Ecosystem | Strengths |
|---|---|---|
| Signac | Seurat | Integration with scRNA-seq, familiar API |
| ArchR | Standalone | Memory efficient, comprehensive |
| chromVAR | Bioconductor | TF motif deviation scoring |
| SnapATAC2 | Python | Fast, scalable |
Signac (R/Seurat)
Goal: Process scATAC-seq data through QC, normalization, dimensionality reduction, and clustering to identify cell types by chromatin accessibility.
Approach: Create a ChromatinAssay from a peak-barcode matrix with fragment files, compute QC metrics (TSS enrichment, nucleosome signal), normalize with TF-IDF, reduce dimensions with LSI (SVD), then cluster and annotate using gene activity scores.
Peak Calling per Cluster
peaks <- CallPeaks(obj, group.by = 'seurat_clusters', macs2.path = '/path/to/macs2')
Use cases
- Use when analyzing single-cell ATAC-seq data.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- preprocessing - scRNA-seq QC (similar concepts)
- clustering - Clustering approaches (shared with scRNA-seq)
- multimodal-integration - Joint scRNA+scATAC analysis
- atac-seq - Bulk ATAC-seq methods
- chip-seq/motif-analysis - Motif databases and analysis
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