Multi Omics Integration

Multi-Omics Integration

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially MOFA+-style and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for integrating matched or partially matched omics layers into shared latent structure and cross-modal interpretation.

When To Use This Skill

• use when the task is multi-omics factor discovery or integrated cohort analysis
• use when the user has two or more omics modalities that should be related jointly
• use when cross-modal factors or harmonized sample structure are needed

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• multiple omics matrices
• sample metadata
• feature mapping resources

Expected Outputs

• integrated latent factors
• cross-modal associations
• integrated visualizations

Preferred Tools

• MOFA+-style approaches
• mixOmics-style approaches
• pandas
• numpy

Starter Pattern

Preferred starting point: MOFA+-style
Inputs: multiple omics matrices, sample metadata, feature mapping resources
Outputs: integrated latent factors, cross-modal associations, integrated visualizations

Workflow

1. Check sample and feature alignment

Confirm which samples are shared and how features relate across modalities.

2. Normalize per modality

Handle each omics layer according to its data-generating properties before integration.

3. Choose integration model

Use a factor-based or correlation-based method matched to the question and data structure.

4. Interpret latent factors

Link integrated components back to biology, covariates, and modality-specific loadings.

5. Export separated artifacts

Save factors, loadings, and modality-aware summaries.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• integrated latent factors
• cross-modal associations
• integrated visualizations

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Verify that modalities, samples, and model assumptions align before integration or inference.
• Export factors, scores, or model outputs together with interpretation context.

Anti-Patterns

• forcing direct feature comparability across unrelated omics types
• ignoring modality-specific QC before integration
• reporting latent factors without biological interpretation or covariate review

Related Skills

• Pathway Analysis
• Systems Biology
• Causal Genomics
• Machine Learning For Omics

Optional Supplements

• reactome-database
• string-database