integrative-analysis
Plan and execute integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows. Use when the user wants to combine experiments, perform cross-dataset comparison, multi-omic integration, peak overlap analysis, differential binding, signal correlation, chromatin state segmentation, enhancer-gene linkage, or any analysis that requires merging or comparing data from two or more ENCODE experiments. Covers same-assay cross-sample, multi-omic same-sample, cross-organism, and perturbation integration designs. Guides compatibility checks, batch effect detection, n
npx skills add majiayu000/claude-skill-registry --skill integrative-analysis-ammawla-encode-toolkit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Plans and executes integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows, including compatibility checks, strategy selection, and provenance documentation.
How it works
- Guides users to define an integration question across four fundamental designs: same assay cross-sample, multi-omic same sample, cross-organism, and perturbation/condition.
- Recommends data discovery via encode_get_facets to assess data availability, then encode_search_experiments to assemble candidate experiments by arm of the integration.
- Performs pairwise compatibility checks with encode_track_experiment and encode_compare_experiments, evaluating organism, assembly, biosample, lab, pipeline, and replicates; mandates that all experiments share the same genome assembly.
- For file selection, uses encode_list_files to retrieve files matching assembly, output type, and format, and enforces rules for matching: same assembly, same output type, same file format, and preferred pipeline version when possible.
- Presents a file-type compatibility matrix to filter feasible combinations and notes cannot directly combine raw FASTQ with processed peaks or mixed assemblies without liftOver.
- Addresses batch effects with detection and mitigation guidance (PCA, lab/date effects) and lists recommended methods (ComBat, SVA, limma removeBatchEffect, matched design).
- Guides choosing an integration strategy (peak overlap, signal correlation, differential binding, differential expression, chromatin state, enhancer-gene linkage, multi-omic overlay) with explicit design rules and example workflows.
- Provides concrete execution steps for common analyses: BEDTools overlap, deepTools multiBigwigSummary, plotCorrelation, and DESeq2/DiffBind workflows, including example commands and R code blocks.
When to use it
- When the user wants to combine multiple ENCODE experiments for cross-dataset analysis or multi-omic planning.
- When interested in peak overlap, differential binding, cross-omic integration, chromatin state segmentation, or enhancer-gene linkage analyses, across same-sample, cross-sample, cross-organism, or perturbation designs.
- When needing to verify data compatibility, normalization choices, and provenance documentation.
What it can touch
- encode_get_facets
- encode_search_experiments
- encode_track_experiment
- encode_list_files
- BEDTools (bedtools intersect, multiIntersect)
- deepTools (multiBigwigSummary, plotCorrelation, plotPCA)
- R packages: DiffBind, DESeq2, sva, limma (for batch handling)
Caveats
- Requires same genome assembly across all experiments (cannot mix GRCh38 and hg19 without liftOver).
- Batch effects can confound results; mitigation requires careful experimental design and reporting of corrections.
- Some integration methods expect minimum replicates (e.g., ≥2 per condition for differential analyses; ≥3 for RNA-seq differential expression) and compatible file types across arms.
- The skill is MIT licensed and uses the claude-code tool; explicit limitations about tool versions and data availability are implicit in the steps.
# Integrative Analysis of ENCODE Data ## When to Use - User wants to combine multiple ENCODE experiments for cross-dataset analysis - User asks about "integrating", "combining", or "comparing" experiments - User wants to overlay histone marks with accessibility or expression data - User needs to plan a multi-omic analysis using ENCODE data - User asks about peak overlap, differential binding, or signal correlation - User wants to perform ChromHMM segmentation using ENCODE histone data Help the user combine multiple ENCODE experiments for cross-dataset or multi-omic analysis. This skill covers the full integration workflow: from defining the question and selecting compatible experiments, through choosing the right integration strategy and tools, to validating results and documenting provenance. ## Literature Foundation | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | ENCODE Phase 3 (2020) | Nature | Registry of 926,535 candidate cis-regulatory elements; integrative analysis framework across 5,992 experiments | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) | ~1,656 | | Gorkin et al. (20
- When to Use
- Literature Foundation
- Step 1: Define the Integration Question
- Step 2: Find Compatible Experiments
- 2a. Explore Data Availability
- 2b. Search for Candidate Experiments
- Step 3: Check Pairwise Compatibility
- Step 4: Select Matched Files
- File Matching Rules
- File Type Compatibility Matrix
- Step 5: Assess and Control Batch Effects
- 5a. Detection
- 5b. Mitigation
- 5c. When NOT to Correct
Using deepTools (Ramirez et al. 2016): multiBigwigSummary bins \ plotPCA -in signal_matrix.npz \ Remove blacklisted regions first (Amemiya et al. 2019) bedtools intersect -a peaks_A.narrowPeak -b hg38-blacklist.v2.bed -v > peaks_A.clean.bed bedtools intersect -a peaks_B.narrowPeak -b hg38-blacklist.v2.bed -v > peaks_B.clean.bed Find overlapping peaks (minimum 1bp overlap) bedtools intersect -a peaks_A.clean.bed -b peaks_B.clean.bed -wa -u > overlap_A_in_B.bed Report overlap statistics both directions A in B: what fraction of A peaks overlap B?
What does the integrative-analysis skill do?
Plan and execute integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows. Use when the user wants to combine experiments, perform cross-dataset comparison, multi-omic integration, peak overlap analysis, differential binding, signal correlation, chromatin state segmentation, enhancer-gene linkage, or any analysis that requires merging or comparing data from two or more ENCODE experiments. Covers same-assay cross-sample, multi-omic same-sample, cross-organism, and perturbation integration designs. Guides compatibility checks, batch effect detection, n
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill integrative-analysis-ammawla-encode-toolkit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.