single-cell-atac

Name: single-cell-atac
Author: GPTomics

1.1kstars185forks

How to install single-cell-atac?

gptomics/bioskills/single-cell-atac

$npx -y skills add GPTomics/bioSkills --skill single-cell-atac

Installs into the current project.

Summary

Process and analyze single-cell ATAC-seq data with Signac, ArchR, SnapATAC2, or Cell Ranger ATAC. Use when handling 10X scATAC or 10X Multiome (paired RNA+ATAC) data, performing per-cell QC, choosing between ArchR/Signac/SnapATAC2 ecosystems, building per-cluster consensus peakse

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SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: Cell Ranger ATAC 2.1+, Signac 1.13+, Seurat 5.0+, ArchR 1.0.2+, SnapATAC2 2.8+, AMULET 1.1+, scDblFinder 1.16+, scater 1.30+, scvi-tools 1.1+, GenomicRanges 1.54+, JASPAR2024 0.99+, BSgenome.Hsapiens.UCSC.hg38 1.4+, EnsDb.Hsapiens.v86 2.99+, MACS3 3.0+. SnapATAC2 2.8+ uses `pp.import_fragments`; older 2.5-2.7 used `pp.import_data` (renamed/removed in 2.9).
4
5	Verify before use:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
8	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
9
10	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
11
12	# Single-Cell ATAC-seq
13
14	"Process my 10X scATAC data from cellranger output" -> Build a per-cell fragment matrix, compute per-cell QC, dimensionality reduction (TF-IDF + LSI / spectral / autoencoder), cluster, call cluster-level pseudobulk peaks, annotate cell types via gene-activity scores, and integrate with paired scRNA-seq if Multiome.
15
16	- R: `Signac::CreateChromatinAssay()` -> `Seurat` workflow (TF-IDF + SVD + UMAP + Leiden)
17	- R: `ArchR::createArrowFiles()` -> ArchR project (TileMatrix + LSI + UMAP)
18	- Python: `snapatac2.pp.import_fragments()` -> SnapATAC2 (spectral / diffusion-map clustering)
19	- CLI (preprocessing): `cellranger-atac count` (10X) or `chromap` (alignment-only fragment files)
20
21	## Ecosystem Choice (The Most Important Decision)
22
23	\| Ecosystem \| Language \| Strength \| Fails when \| Best for \|
24	\|-----------\|---------\|---------\|------------\|----------\|
25	\| Signac (Stuart 2021) \| R, Seurat-based \| Tightest scRNA-seq integration; Seurat ecosystem mature \| Memory hungry on >100K cells; slower than ArchR \| Multiome RNA+ATAC; small-to-medium datasets; Seurat user \|
26	\| ArchR (Granja 2021) \| R, Arrow/HDF5 \| Memory-efficient (Arrow files); fast on 100K-1M cells; built-in trajectory + doublet \| Less RNA-seq integration; ArchR-specific format \| Large bulk-cohort scATAC; trajectory analysis; ATAC-only \|
27	\| SnapATAC2 (Zhang 2024) \| Python, AnnData \| Memory-efficient; modern Python ecosystem; spectral clustering performant \| Newer; benchmarks evolving; ecosystem smaller than R \| Python-first labs; very large datasets (>1M cells) \|
28	\| Cell Ranger ATAC \| CLI (10X-specific) \| 10X official preprocessing \| Closed; fixed pipeline \| Only as preprocessing step; analysis happens elsewhere \|
29	\| scATAC-pro \| CLI-based pipeline \| Alternative preprocessing \| Less maintained \| Legacy; not recommended for new projects \|
30
31	Methodology evolves; verify against Granja 2021 (ArchR), Stuart 2021 (Signac), Zhang 2024 (SnapATAC2), Heumos 2023 (best practices) before locking pipelines.
32
33	## 10X Multiome Caveat (Paired RNA + ATAC)
34
35	10X Multiome chemistry profiles RNA AND ATAC from the same cell. Outputs are joined by a shared barcode. Multiome workflows use Signac for ATAC and Seurat for RNA, integrated through the same Seurat object via WNN (Weighted Nearest Neighbor; Hao 2021).
36
37	Single-modality 10X scATAC (chemistry v1, v2) does NOT produce paired RNA. Verify the chemistry on the cellranger summary before assuming Multiome.
38
39	## Per-Cell QC Thresholds
40
41	\| Metric \| Definition \| Pass \| Caution \| Reject \| Source \|
42	\|--------\|-----------\|------\|---------\|--------\|--------\|
43	\| Fragment count per cell \| n_fragments after dedup \| 3000-50000 \| 1000-3000 \| < 1000 or > 80000 \| 10X recommendation; high = doublet \|
44	\| TSS enrichment per cell \| Signal at TSS / flanks (per cell) \| >= 4 \| 2-4 \| < 2 \| ArchR / Signac default; lower than bulk because per-cell \|
45	\| Nucleosome signal \| Mono / NFR fragment ratio \| <= 4 \| 4-10 \| > 10 \| Signac default; high = poor library \|
46	\| % reads in peaks (per cell) \| FRiP per cell at consensus \| >= 0.15 \| 0.10-0.15 \| < 0.05 \| ArchR / Signac defaults \|
47	\| Mitochondrial fraction (per cell) \| chrM / total per cell \| < 0.05 \| 0.05-0.15 \| > 0.20 \| Lower than bulk; per-cell more sensitive \|
48	\| Doublet score \| AMULET / ArchR doublet \| < 0.5 \| 0.5-0.7 \| > 0.7 \| Tool-dependent threshold \|
49	\| Blacklist ratio \| Reads in ENCODE blacklist / total \| < 0.05 \| 0.05-0.10 \| > 0.10 \| Standard \|
50
51	Per-cell thresholds are looser than bulk because individual cells have orders of magnitude less signal; the population aggregate is what matters.
52
53	## Doublet Detection: Three Approaches
54
55	\| Tool \| Method \| Strength \| Fails when \|
56	\|------\|--------\|---------\|------------\|
57	\| AMULET (Thibodeau 2021) \| Coll

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single-cell-atac

1.1kstars185forks

View on GitHub

How to install single-cell-atac?

gptomics/bioskills/single-cell-atac

$npx -y skills add GPTomics/bioSkills --skill single-cell-atac

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: Cell Ranger ATAC 2.1+, Signac 1.13+, Seurat 5.0+, ArchR 1.0.2+, SnapATAC2 2.8+, AMULET 1.1+, scDblFinder 1.16+, scater 1.30+, scvi-tools 1.1+, GenomicRanges 1.54+, JASPAR2024 0.99+, BSgenome.Hsapiens.UCSC.hg38 1.4+, EnsDb.Hsapiens.v86 2.99+, MACS3 3.0+. SnapATAC2 2.8+ uses `pp.import_fragments`; older 2.5-2.7 used `pp.import_data` (renamed/removed in 2.9).
4
5	Verify before use:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
8	- CLI: `<tool> --version` then `<tool> --help` to confirm flags
9
10	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
11
12	# Single-Cell ATAC-seq
13
14	"Process my 10X scATAC data from cellranger output" -> Build a per-cell fragment matrix, compute per-cell QC, dimensionality reduction (TF-IDF + LSI / spectral / autoencoder), cluster, call cluster-level pseudobulk peaks, annotate cell types via gene-activity scores, and integrate with paired scRNA-seq if Multiome.
15
16	- R: `Signac::CreateChromatinAssay()` -> `Seurat` workflow (TF-IDF + SVD + UMAP + Leiden)
17	- R: `ArchR::createArrowFiles()` -> ArchR project (TileMatrix + LSI + UMAP)
18	- Python: `snapatac2.pp.import_fragments()` -> SnapATAC2 (spectral / diffusion-map clustering)
19	- CLI (preprocessing): `cellranger-atac count` (10X) or `chromap` (alignment-only fragment files)
20
21	## Ecosystem Choice (The Most Important Decision)
22
23	\| Ecosystem \| Language \| Strength \| Fails when \| Best for \|
24	\|-----------\|---------\|---------\|------------\|----------\|
25	\| Signac (Stuart 2021) \| R, Seurat-based \| Tightest scRNA-seq integration; Seurat ecosystem mature \| Memory hungry on >100K cells; slower than ArchR \| Multiome RNA+ATAC; small-to-medium datasets; Seurat user \|
26	\| ArchR (Granja 2021) \| R, Arrow/HDF5 \| Memory-efficient (Arrow files); fast on 100K-1M cells; built-in trajectory + doublet \| Less RNA-seq integration; ArchR-specific format \| Large bulk-cohort scATAC; trajectory analysis; ATAC-only \|
27	\| SnapATAC2 (Zhang 2024) \| Python, AnnData \| Memory-efficient; modern Python ecosystem; spectral clustering performant \| Newer; benchmarks evolving; ecosystem smaller than R \| Python-first labs; very large datasets (>1M cells) \|
28	\| Cell Ranger ATAC \| CLI (10X-specific) \| 10X official preprocessing \| Closed; fixed pipeline \| Only as preprocessing step; analysis happens elsewhere \|
29	\| scATAC-pro \| CLI-based pipeline \| Alternative preprocessing \| Less maintained \| Legacy; not recommended for new projects \|
30
31	Methodology evolves; verify against Granja 2021 (ArchR), Stuart 2021 (Signac), Zhang 2024 (SnapATAC2), Heumos 2023 (best practices) before locking pipelines.
32
33	## 10X Multiome Caveat (Paired RNA + ATAC)
34
35	10X Multiome chemistry profiles RNA AND ATAC from the same cell. Outputs are joined by a shared barcode. Multiome workflows use Signac for ATAC and Seurat for RNA, integrated through the same Seurat object via WNN (Weighted Nearest Neighbor; Hao 2021).
36
37	Single-modality 10X scATAC (chemistry v1, v2) does NOT produce paired RNA. Verify the chemistry on the cellranger summary before assuming Multiome.
38
39	## Per-Cell QC Thresholds
40
41	\| Metric \| Definition \| Pass \| Caution \| Reject \| Source \|
42	\|--------\|-----------\|------\|---------\|--------\|--------\|
43	\| Fragment count per cell \| n_fragments after dedup \| 3000-50000 \| 1000-3000 \| < 1000 or > 80000 \| 10X recommendation; high = doublet \|
44	\| TSS enrichment per cell \| Signal at TSS / flanks (per cell) \| >= 4 \| 2-4 \| < 2 \| ArchR / Signac default; lower than bulk because per-cell \|
45	\| Nucleosome signal \| Mono / NFR fragment ratio \| <= 4 \| 4-10 \| > 10 \| Signac default; high = poor library \|
46	\| % reads in peaks (per cell) \| FRiP per cell at consensus \| >= 0.15 \| 0.10-0.15 \| < 0.05 \| ArchR / Signac defaults \|
47	\| Mitochondrial fraction (per cell) \| chrM / total per cell \| < 0.05 \| 0.05-0.15 \| > 0.20 \| Lower than bulk; per-cell more sensitive \|
48	\| Doublet score \| AMULET / ArchR doublet \| < 0.5 \| 0.5-0.7 \| > 0.7 \| Tool-dependent threshold \|
49	\| Blacklist ratio \| Reads in ENCODE blacklist / total \| < 0.05 \| 0.05-0.10 \| > 0.10 \| Standard \|
50
51	Per-cell thresholds are looser than bulk because individual cells have orders of magnitude less signal; the population aggregate is what matters.
52
53	## Doublet Detection: Three Approaches
54
55	\| Tool \| Method \| Strength \| Fails when \|
56	\|------\|--------\|---------\|------------\|
57	\| AMULET (Thibodeau 2021) \| Coll

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