motif-deviation

Name: motif-deviation
Author: GPTomics

1.1kstars185forks

How to install motif-deviation?

gptomics/bioskills/motif-deviation

$npx -y skills add GPTomics/bioSkills --skill motif-deviation

Installs into the current project.

Summary

Analyze TF motif accessibility variability across samples or single cells using chromVAR. Use when identifying TF motifs whose accessibility correlates with conditions, computing per-sample motif z-scores after matched background correction, comparing to ArchR / Signac equivalent

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SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: chromVAR 1.24+, motifmatchr 1.24+, JASPAR2024 0.99+, TFBSTools 1.40+, BSgenome.Hsapiens.UCSC.hg38 1.4+, SummarizedExperiment 1.32+, limma 3.58+, ggplot2 3.5+, Matrix 1.6+, ArchR 1.0.2+, Signac 1.13+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
7
8	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
9
10	# Motif Deviation (chromVAR)
11
12	"Which TF motifs explain accessibility variation across my samples or cells?" -> Compute per-sample (or per-cell) deviation z-scores: how many standard deviations above expectation each TF motif's accessibility falls, controlling for GC content and overall accessibility via matched background peak sets.
13
14	- R: `chromVAR::computeDeviations(counts, motifs)` -> per-sample z-scores
15	- R: `chromVAR::computeVariability(dev)` -> per-motif variance ranking
16	- Single-cell alternative: `Signac::RunChromVAR()` (wrapper with matched defaults) or `ArchR::addDeviationsMatrix()`
17
18	chromVAR answers a different question than footprinting: footprinting asks "is this specific motif site bound?", chromVAR asks "do peaks containing this motif have systematically more or less accessibility than expected?" The two are complementary.
19
20	## What chromVAR Computes
21
22	For each (motif, sample) pair:
23	- Raw deviation = Sum of accessibility at peaks containing the motif - expected from a matched-GC, matched-accessibility background.
24	- Bias-corrected deviation = Raw deviation / SD of background deviations.
25	- Z-score = (corrected deviation - mean across cells) / SD across cells. Reported as the principal output.
26
27	Z-scores are signed: positive = motif more accessible in this sample than population average; negative = less. Magnitudes 2-5 are typical for biologically interesting motifs; >5 indicates strong covariation with sample state.
28
29	## Algorithmic Taxonomy
30
31	\| Tool \| Input \| Background \| Output \| Best for \| Fails when \|
32	\|------\|-------\|------------\|--------\|----------\|------------\|
33	\| chromVAR \| Peak count matrix + motif annotations \| Matched GC + accessibility (50 peaks per match by default) \| Per-sample motif z-score \| Bulk + single-cell (sparse-aware); cross-sample variability \| < 1500 reads/sample (bulk) or < 500 cells/cluster (sc); too few peaks (< 5000) \|
34	\| Signac::RunChromVAR \| Seurat single-cell ATAC object \| Same as chromVAR (delegated) \| Motif assay in Seurat object \| Standard single-cell workflows in Seurat ecosystem \| Same as chromVAR; needs Seurat object setup \|
35	\| ArchR::addDeviationsMatrix \| ArrowFile + tile/peak matrix \| ArchR's getBgdPeaks (matched on GC + log accessibility) \| Per-cell deviation matrix in ArchR project \| ArchR ecosystem; faster on large scATAC \| ArchR-specific format; not portable to chromVAR objects \|
36	\| Signac::FindMarkers (with motifs as features) \| Motif accessibility matrix from RunChromVAR \| Per-cell-cluster \| Differential motifs per cluster \| Cluster-level differential \| Differential test must be on z-scores; raw counts will mislead \|
37	\| TF activity inference (DecoupleR / SCENIC+) \| Gene expression + motif activity \| Multi-modal \| TF activity score \| Multi-omics integration \| Requires paired RNA-seq; chromVAR alone is insufficient \|
38
39	Methodology evolves; verify against current chromVAR (Schep 2017), ArchR (Granja 2021), and Signac (Stuart 2021) benchmarks before locking pipelines.
40
41	## chromVAR vs Footprinting -- Different Questions
42
43	\| Question \| Tool \|
44	\|----------\|------\|
45	\| Does the bulk pattern of motif-containing peaks vary with condition? \| chromVAR \|
46	\| Is THIS specific motif site bound by a TF? \| TOBIAS / HINT-ATAC \|
47	\| Per-cell TF activity in scATAC \| chromVAR (via Signac/ArchR) \|
48	\| Per-cell TF binding at specific sites \| scprinter \|
49	\| TF activity correlated with gene expression \| chromVAR + co-expression OR SCENIC+ \|
50	\| Which TF families distinguish my cell clusters? \| chromVAR per-cluster z-scores \|
51	\| Differential bound vs unbound between conditions \| TOBIAS BINDetect \|
52
53	chromVAR is fundamentally a summary statistic over many motif sites. Footprinting is per-site classification. Use chromVAR when motif site count >> 100; use footprinting when specific sites matter.
54
55	## Per-Tool Failure Modes
56
57	### chromVAR -- Too few peaks or too few reads
58
59	Trigger: ATAC peakset < 5000 peaks; per-sample read depth < 1500 in peaks.
60
61	Mechanism: chromVAR's background sampling requires enough peaks to find matched GC + access

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motif-deviation

1.1kstars185forks

View on GitHub

How to install motif-deviation?

gptomics/bioskills/motif-deviation

$npx -y skills add GPTomics/bioSkills --skill motif-deviation

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: chromVAR 1.24+, motifmatchr 1.24+, JASPAR2024 0.99+, TFBSTools 1.40+, BSgenome.Hsapiens.UCSC.hg38 1.4+, SummarizedExperiment 1.32+, limma 3.58+, ggplot2 3.5+, Matrix 1.6+, ArchR 1.0.2+, Signac 1.13+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
7
8	If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
9
10	# Motif Deviation (chromVAR)
11
12	"Which TF motifs explain accessibility variation across my samples or cells?" -> Compute per-sample (or per-cell) deviation z-scores: how many standard deviations above expectation each TF motif's accessibility falls, controlling for GC content and overall accessibility via matched background peak sets.
13
14	- R: `chromVAR::computeDeviations(counts, motifs)` -> per-sample z-scores
15	- R: `chromVAR::computeVariability(dev)` -> per-motif variance ranking
16	- Single-cell alternative: `Signac::RunChromVAR()` (wrapper with matched defaults) or `ArchR::addDeviationsMatrix()`
17
18	chromVAR answers a different question than footprinting: footprinting asks "is this specific motif site bound?", chromVAR asks "do peaks containing this motif have systematically more or less accessibility than expected?" The two are complementary.
19
20	## What chromVAR Computes
21
22	For each (motif, sample) pair:
23	- Raw deviation = Sum of accessibility at peaks containing the motif - expected from a matched-GC, matched-accessibility background.
24	- Bias-corrected deviation = Raw deviation / SD of background deviations.
25	- Z-score = (corrected deviation - mean across cells) / SD across cells. Reported as the principal output.
26
27	Z-scores are signed: positive = motif more accessible in this sample than population average; negative = less. Magnitudes 2-5 are typical for biologically interesting motifs; >5 indicates strong covariation with sample state.
28
29	## Algorithmic Taxonomy
30
31	\| Tool \| Input \| Background \| Output \| Best for \| Fails when \|
32	\|------\|-------\|------------\|--------\|----------\|------------\|
33	\| chromVAR \| Peak count matrix + motif annotations \| Matched GC + accessibility (50 peaks per match by default) \| Per-sample motif z-score \| Bulk + single-cell (sparse-aware); cross-sample variability \| < 1500 reads/sample (bulk) or < 500 cells/cluster (sc); too few peaks (< 5000) \|
34	\| Signac::RunChromVAR \| Seurat single-cell ATAC object \| Same as chromVAR (delegated) \| Motif assay in Seurat object \| Standard single-cell workflows in Seurat ecosystem \| Same as chromVAR; needs Seurat object setup \|
35	\| ArchR::addDeviationsMatrix \| ArrowFile + tile/peak matrix \| ArchR's getBgdPeaks (matched on GC + log accessibility) \| Per-cell deviation matrix in ArchR project \| ArchR ecosystem; faster on large scATAC \| ArchR-specific format; not portable to chromVAR objects \|
36	\| Signac::FindMarkers (with motifs as features) \| Motif accessibility matrix from RunChromVAR \| Per-cell-cluster \| Differential motifs per cluster \| Cluster-level differential \| Differential test must be on z-scores; raw counts will mislead \|
37	\| TF activity inference (DecoupleR / SCENIC+) \| Gene expression + motif activity \| Multi-modal \| TF activity score \| Multi-omics integration \| Requires paired RNA-seq; chromVAR alone is insufficient \|
38
39	Methodology evolves; verify against current chromVAR (Schep 2017), ArchR (Granja 2021), and Signac (Stuart 2021) benchmarks before locking pipelines.
40
41	## chromVAR vs Footprinting -- Different Questions
42
43	\| Question \| Tool \|
44	\|----------\|------\|
45	\| Does the bulk pattern of motif-containing peaks vary with condition? \| chromVAR \|
46	\| Is THIS specific motif site bound by a TF? \| TOBIAS / HINT-ATAC \|
47	\| Per-cell TF activity in scATAC \| chromVAR (via Signac/ArchR) \|
48	\| Per-cell TF binding at specific sites \| scprinter \|
49	\| TF activity correlated with gene expression \| chromVAR + co-expression OR SCENIC+ \|
50	\| Which TF families distinguish my cell clusters? \| chromVAR per-cluster z-scores \|
51	\| Differential bound vs unbound between conditions \| TOBIAS BINDetect \|
52
53	chromVAR is fundamentally a summary statistic over many motif sites. Footprinting is per-site classification. Use chromVAR when motif site count >> 100; use footprinting when specific sites matter.
54
55	## Per-Tool Failure Modes
56
57	### chromVAR -- Too few peaks or too few reads
58
59	Trigger: ATAC peakset < 5000 peaks; per-sample read depth < 1500 in peaks.
60
61	Mechanism: chromVAR's background sampling requires enough peaks to find matched GC + access

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