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数据与复现
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bio-single-cell-scatac-analysis
维护者 FreedomIntelligence · 最近更新 2026年4月1日
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.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-single-cell-scatac-analysis
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- R：Signac：：CreateChromatinAssay() → RunTFIDF() → FindTopFeatures() → RunSVD()。
- R：ArchR：：createArrowFiles() ，用于 large 数据集s。
- 分析 my single-cell ATAC-seq data" → Process peak-barcode matrices，perform QC/filtering，reduce dimensions ，支持 LSI，cluster cells，call peaks per cluster，、 score motif activity. R：Signac：：CreateChromatinAssay() → RunTFIDF() → FindTopFeatures() → RunSVD() R：ArchR：：createArrowFiles() ，用于 large 数据集s。
- 分析 single-cell chromatin accessibility data to identify cell types 、 regulatory elements。
- peak_counts <- FeatureMatrix(fragments = Fragments(obj)，features = peaks，cells = colnames(obj))。
原始文档
SKILL.md 摘录
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')
适用场景
- 适合在analyzing single-cell ATAC-seq data时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- 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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