bio-multi-omics-mixomics-analysis

Name: bio-multi-omics-mixomics-analysis
Availability: InStock
Author: dailycafi

Integrate and classify multi-omics datasets with mixOmics supervised methods.

7|Updated Apr 5, 2026

Search Tags:#classification #r #multi-omics #feature-selection #diablo #mixomics #spls

Ready to run

npx skills add https://github.com/dailycafi/metabolism-skills --skill bio-multi-omics-mixomics-analysis-dailycafi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bio-multi-omics-mixomics-analysis
Source: https://github.com/dailycafi/metabolism-skills/tree/main/skills/multi-omics/mixomics-analysis
Command: npx skills add https://github.com/dailycafi/metabolism-skills --skill bio-multi-omics-mixomics-analysis-dailycafi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Integrate and classify multi-omics datasets to identify discriminant features and biomarker panels that separate experimental groups, removing the need for ad-hoc per-omics analyses and manual cross-omics comparison.

Core Features & Use Cases

Supervised multi-block integration: Build DIABLO models (block.splsda) to find multi-omics signatures that discriminate between conditions.
Pairwise and single-omics methods: Use sPLS for correlated feature discovery, sPLS-DA/splsda for single-omics classification, and spca for sparse dimensionality reduction.
Parameter tuning and validation: Tune keepX via cross-validation, assess performance with perf and auroc, and export selected variables for downstream pathway analysis.
Visualization and export: Generate consensus and per-block sample plots, circos plots, correlation networks, heatmaps, and CSV exports of selected features.

Quick Start

Classify samples using DIABLO on my RNA and protein matrices to obtain selected features per block and diagnostic plots.

Frequently Asked Questions about bio-multi-omics-mixomics-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I integrate multi-omics datasets for classification in R?▼

You can integrate multi-omics datasets for classification by building DIABLO models with block.splsda to find discriminant multi-omics signatures and biomarker panels that separate experimental groups.

What is DIABLO used for in multi-omics feature selection?▼

DIABLO is a supervised multi-block integration method used to identify cross-omics biomarker panels that discriminate between conditions, eliminating ad-hoc per-omics analyses and manual cross-omics comparison.

Can I use mixOmics methods for single-omics classification?▼

Yes, you can use sPLS-DA for single-omics classification, sPLS for pairwise correlated feature discovery, and spca for sparse dimensionality reduction on single-omics datasets.

How do I tune keepX parameters and validate multi-omics models?▼

Tune keepX via cross-validation, assess model performance using the perf function and auroc, and export the selected variables to validate discriminant features for downstream analysis.

What visualizations can I generate for multi-omics integration results?▼

You can generate consensus and per-block sample plots, circos plots, correlation networks, and heatmaps to visualize cross-omics feature correlations and sample separations.

Does this approach support multi-study transcriptomics and metabolomics integration?▼

Yes, MINT supports multi-study integration for transcriptomics, proteomics, and metabolomics datasets, allowing you to classify samples and identify discriminant features across different studies.