bio-applied-data-harmonization

SKILLWorkflowcommunity
v0.0.0Pavel-KravchenkoUnknownUpdated 3mo agoSource →

Harmonize multi-omics data (RNA-seq, proteomics, methylation, metabolomics) before integration — per-layer normalization, KNN/half-minimum missing-value imputation, PCA/PVCA batch-effect detection, and ComBat correction with pandas/scikit-learn. Use when prepping matrices for MOFA2/DIABLO/mixOmics,

Community-submitted skill. Not yet reviewed by the Forge team. Full prompt content may not be available.Request review →
4Repo stars
1Clients
1Formats
3mo agoLast update
Skill
AuthorPavel-Kravchenko
Version0.0.0
LicenseUnknown
CategoryWorkflow
Formatsskill.md
PromptNot published
Compatibility
Claude✓ Supported
Cursor—
Copilot—
ChatGPT—
Gemini—
About

Harmonize multi-omics data (RNA-seq, proteomics, methylation, metabolomics) before integration — per-layer normalization, KNN/half-minimum missing-value imputation, PCA/PVCA batch-effect detection, and ComBat correction with pandas/scikit-learn. Use when prepping matrices for MOFA2/DIABLO/mixOmics, fixing missing values in proteomics MS data, or removing batch effects confounded with sequencing da

Keywords
skillclaude

No dependency coverage

This entry publishes no npm package, so Forge has no dependency tree for it. That is a gap in coverage — not a statement that it has no dependencies.