differential-accessibility

Name: differential-accessibility
Author: GPTomics

1.1kstars185forks

How to install differential-accessibility?

gptomics/bioskills/differential-accessibility

$npx -y skills add GPTomics/bioSkills --skill differential-accessibility

Installs into the current project.

Summary

Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full

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SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: DiffBind 3.12+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, limma 3.58+, GenomicRanges 1.54+, ChIPseeker 1.38+, Subread 2.0+ (featureCounts), sva 3.50+, RUVSeq 1.36+.
4
5	Before using code patterns, verify installed versions match:
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	# Differential Accessibility
11
12	"Find chromatin regions that change accessibility between my conditions" -> Build a sample-by-region count matrix, normalize for library size and chromatin compaction, fit a generalized linear model (negative-binomial), and extract regions with significant accessibility change.
13
14	- R (consensus-peak workflow): `DiffBind` -> count -> normalize -> contrast -> analyze
15	- R (window-based, no peak set): `csaw::windowCounts` + `filterWindowsGlobal` + edgeR QL F-test
16	- R (existing peak-count matrix): `DESeq2` or `edgeR` directly on `featureCounts` output
17
18	DiffBind is a wrapper around DESeq2 / edgeR with ATAC-aware defaults. csaw is the only peak-free option; it tests fixed-width sliding windows. The choice depends on whether peaks are stable across conditions (use DiffBind) or whether some condition has dramatically different peak structure (use csaw or rebuild consensus peaks).
19
20	## Algorithmic Taxonomy
21
22	\| Tool \| Model \| Input \| Min reps \| Strength \| Fails when \|
23	\|------\|-------\|-------\|----------\|----------\|------------\|
24	\| DiffBind 3.x (default DESeq2) \| NB GLM via DESeq2 on consensus peaks \| BAM + peak files \| 2-3 per group \| ATAC-aware defaults; built-in QC; blocking factors. Default in 3.x is `normalize=DBA_NORM_LIB` with `library=DBA_LIBSIZE_FULL` (full library size, background-included) \| Peaks differ dramatically between conditions (closed -> open shifts width); fewer than 2 reps per group \|
25	\| DiffBind with edgeR backend \| NB GLM via edgeR-QL on consensus peaks \| Same \| 2-3 per group \| Robust at low replicates (n=2 OK); QL test calibrates dispersion better than DESeq2 at small n \| When global accessibility shifts dominate, switch to spike-in or full-library (library=DBA_LIBSIZE_FULL), never reads-in-peaks \|
26	\| DESeq2 directly on peak counts \| NB GLM with shrinkage \| featureCounts SAF \| 3+ \| Maximum control; integrates with apeglm shrinkage; modern interface \| Need to manually build consensus peakset; per-region pre-filter required (low counts inflate dispersion) \|
27	\| edgeR QL F-test on peak counts \| NB QL (quasi-likelihood) \| featureCounts \| 2 \| Calibrated FDR at low n (n=2 viable); robust to outlier reps \| Manual consensus peakset; small library bias unless normalization explicit \|
28	\| csaw (windows) \| edgeR-QL on sliding windows \| BAM only \| 2 \| No peak set required; detects diffuse changes peaks miss; merges adjacent windows \| Computationally heavy; window size choice biases results; harder to annotate downstream \|
29	\| limma-voom \| linear model with mean-variance trend \| log2(CPM+offset) \| 3 \| Fast; good calibration at moderate count \| Mis-calibrated at very low counts (atac peaks often have dropouts); needs explicit voom normalization \|
30
31	Methodology evolves; verify the current consensus practice (Gontarz 2020 DA-strategy benchmark; Reske 2020 normalization comparison) before locking pipelines.
32
33	## Decision Tree by Experimental Scenario
34
35	\| Scenario \| Recommended workflow \| Why \|
36	\|----------\|---------------------\|-----\|
37	\| 3+ reps, similar peak structure across conditions \| DiffBind (DESeq2 backend), `summits=250`, `normalize=DBA_NORM_NATIVE` \| Standard pattern; peak-level inference is interpretable \|
38	\| 2 reps per condition \| DiffBind with edgeR backend OR raw edgeR QL \| DESeq2 underpowered at n=2; QL is robust \|
39	\| Peak structure differs dramatically (e.g., differentiation, KO of pioneer TF) \| csaw windows OR rebuild consensus peakset post-hoc per condition then take union \| Stable consensus peakset is invalid when chromatin landscape shifts \|
40	\| Multi-factor design (batch, sex, time) \| DiffBind with `dba.contrast(..., design='~Batch + Condition')` \| Standard linear model adjustment \|
41	\| Hidden batch / unknown variance \| DESeq2 + SVA OR RUVseq before fitting \| Empirical surrogate variables capture unknown nuisance \|
42	\| Long timecourse (5+ time points) \| DESeq2 LRT (likelihood ratio test) on `~time + condition + time:condition` \| Captures temporal interaction; use `differential-expression/timeseries-de` patterns \|
43	\| Diffuse / broad ac

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differential-accessibility

1.1kstars185forks

View on GitHub

How to install differential-accessibility?

gptomics/bioskills/differential-accessibility

$npx -y skills add GPTomics/bioSkills --skill differential-accessibility

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: DiffBind 3.12+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, limma 3.58+, GenomicRanges 1.54+, ChIPseeker 1.38+, Subread 2.0+ (featureCounts), sva 3.50+, RUVSeq 1.36+.
4
5	Before using code patterns, verify installed versions match:
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	# Differential Accessibility
11
12	"Find chromatin regions that change accessibility between my conditions" -> Build a sample-by-region count matrix, normalize for library size and chromatin compaction, fit a generalized linear model (negative-binomial), and extract regions with significant accessibility change.
13
14	- R (consensus-peak workflow): `DiffBind` -> count -> normalize -> contrast -> analyze
15	- R (window-based, no peak set): `csaw::windowCounts` + `filterWindowsGlobal` + edgeR QL F-test
16	- R (existing peak-count matrix): `DESeq2` or `edgeR` directly on `featureCounts` output
17
18	DiffBind is a wrapper around DESeq2 / edgeR with ATAC-aware defaults. csaw is the only peak-free option; it tests fixed-width sliding windows. The choice depends on whether peaks are stable across conditions (use DiffBind) or whether some condition has dramatically different peak structure (use csaw or rebuild consensus peaks).
19
20	## Algorithmic Taxonomy
21
22	\| Tool \| Model \| Input \| Min reps \| Strength \| Fails when \|
23	\|------\|-------\|-------\|----------\|----------\|------------\|
24	\| DiffBind 3.x (default DESeq2) \| NB GLM via DESeq2 on consensus peaks \| BAM + peak files \| 2-3 per group \| ATAC-aware defaults; built-in QC; blocking factors. Default in 3.x is `normalize=DBA_NORM_LIB` with `library=DBA_LIBSIZE_FULL` (full library size, background-included) \| Peaks differ dramatically between conditions (closed -> open shifts width); fewer than 2 reps per group \|
25	\| DiffBind with edgeR backend \| NB GLM via edgeR-QL on consensus peaks \| Same \| 2-3 per group \| Robust at low replicates (n=2 OK); QL test calibrates dispersion better than DESeq2 at small n \| When global accessibility shifts dominate, switch to spike-in or full-library (library=DBA_LIBSIZE_FULL), never reads-in-peaks \|
26	\| DESeq2 directly on peak counts \| NB GLM with shrinkage \| featureCounts SAF \| 3+ \| Maximum control; integrates with apeglm shrinkage; modern interface \| Need to manually build consensus peakset; per-region pre-filter required (low counts inflate dispersion) \|
27	\| edgeR QL F-test on peak counts \| NB QL (quasi-likelihood) \| featureCounts \| 2 \| Calibrated FDR at low n (n=2 viable); robust to outlier reps \| Manual consensus peakset; small library bias unless normalization explicit \|
28	\| csaw (windows) \| edgeR-QL on sliding windows \| BAM only \| 2 \| No peak set required; detects diffuse changes peaks miss; merges adjacent windows \| Computationally heavy; window size choice biases results; harder to annotate downstream \|
29	\| limma-voom \| linear model with mean-variance trend \| log2(CPM+offset) \| 3 \| Fast; good calibration at moderate count \| Mis-calibrated at very low counts (atac peaks often have dropouts); needs explicit voom normalization \|
30
31	Methodology evolves; verify the current consensus practice (Gontarz 2020 DA-strategy benchmark; Reske 2020 normalization comparison) before locking pipelines.
32
33	## Decision Tree by Experimental Scenario
34
35	\| Scenario \| Recommended workflow \| Why \|
36	\|----------\|---------------------\|-----\|
37	\| 3+ reps, similar peak structure across conditions \| DiffBind (DESeq2 backend), `summits=250`, `normalize=DBA_NORM_NATIVE` \| Standard pattern; peak-level inference is interpretable \|
38	\| 2 reps per condition \| DiffBind with edgeR backend OR raw edgeR QL \| DESeq2 underpowered at n=2; QL is robust \|
39	\| Peak structure differs dramatically (e.g., differentiation, KO of pioneer TF) \| csaw windows OR rebuild consensus peakset post-hoc per condition then take union \| Stable consensus peakset is invalid when chromatin landscape shifts \|
40	\| Multi-factor design (batch, sex, time) \| DiffBind with `dba.contrast(..., design='~Batch + Condition')` \| Standard linear model adjustment \|
41	\| Hidden batch / unknown variance \| DESeq2 + SVA OR RUVseq before fitting \| Empirical surrogate variables capture unknown nuisance \|
42	\| Long timecourse (5+ time points) \| DESeq2 LRT (likelihood ratio test) on `~time + condition + time:condition` \| Captures temporal interaction; use `differential-expression/timeseries-de` patterns \|
43	\| Diffuse / broad ac

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