/integrative-analysis

name: integrative-analysis
description: 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, normalization, integration strategy selection, and provenance documentation.

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. (2020) | Nature | Integrative analysis of 3,158 mouse epigenomes; cross-tissue chromatin state annotation | [10.1038/s41586-020-2093-3](https://doi.org/10.1038/s41586-020-2093-3) | ~301 | | Ernst & Kellis (2012) | Nature Methods | ChromHMM: chromatin state discovery from combinatorial histone mark patterns | [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906) | ~2,294 | | Nasser et al. (2021) | Nature | Activity-by-Contact (ABC) model for enhancer-gene linkage; outperforms proximity assignment | [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x) | ~468 | | Quinlan & Hall (2010) | Bioinformatics | BEDTools: genome arithmetic for interval comparisons, intersections, and merges | [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033) | ~10,000 | | Ramirez et al. (2016) | Nucleic Acids Res | deepTools: signal normalization, correlation, and visualization for multi-sample genomic data | [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257) | ~3,000 | | Love et al. (2014) | Genome Biology | DESeq2: differential analysis of count data with shrinkage estimation | [10.1186/s13059-014-0550-8](https://doi.org/10.1186/s13059-014-0550-8) | ~40,000 | | Ross-Innes et al. (2012) | Nature | DiffBind: differential binding analysis of ChIP-seq peak data across conditions | [10.1038/nature10730](https://doi.org/10.1038/nature10730) | ~1,200 | | Leek et al. (2010) | Nature Rev Genetics | Tackling batch effects: PCA-based detection, SVA/ComBat correction, experimental design | [10.1038/nrg2825](https://doi.org/10.1038/nrg2825) | ~1,200 |

Step 1: Define the Integration Question

Clarify with the user which type of integration they need. There are four fundamental designs:

| Integration Design | Example | Key Challenge | |-------------------|---------|---------------| | **Same assay, cross-sample** | H3K27ac ChIP-seq across 5 tissues | Batch effects between labs/donors | | **Multi-omic, same sample** | ATAC-seq + RNA-seq + ChIP-seq in K562 | Matching file types and normalization | | **Cross-organism** | Human vs mouse liver chromatin | Ortholog mapping, synteny conservation | | **Perturbation / condition** | Before vs after treatment | Need matched replicates per condition |

Each design has different requirements for compatibility, normalization, and statistical framework. Establish the design before searching for data.

**Questions to ask the user**:

• What biological question are you trying to answer?
• Are you comparing across samples (differential) or combining across samples (cataloging)?
• How many conditions/tissues/time points?
• Do you need statistical testing or descriptive overlap?

Step 2: Find Compatible Experiments

2a. Explore Data Availability

Start with `encode_get_facets` to understand what data exists before committing to a design:

encode_get_facets(
    assay_title="Histone ChIP-seq",
    organ="pancreas"
)

This returns counts by target, biosample, lab, and other facets. Use it to verify that the intended comparison has sufficient data on both sides.

2b. Search for Candidate Experiments

Search for experiments matching each arm of the integration:

encode_search_experiments(
    assay_title="Histone ChIP-seq",
    target="H3K27ac",
    organ="pancreas",
    biosample_type="tissue",
    limit=100
)

For multi-omic designs, search each assay layer separately:

# Accessibility layer
encode_search_experiments(assay_title="ATAC-seq", organ="pancreas", limit=50)

# Expression layer
encode_search_experiments(assay_title="total RNA-seq", organ="pancreas", limit=50)

# Histone layer
encode_search_experiments(assay_title="Histone ChIP-seq", target="H3K27ac", organ="pancreas", limit=50)

Present a summary table to the user showing experiments found per arm, number of replicates, labs represented, and any audit flags.

Step 3: Check Pairwise Compatibility

Track candidate experiments and then check compatibility:

encode_track_experiment(accession="ENCSR...")
encode_track_experiment(accession="ENCSR...")

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