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bio-multi-omics-data-harmonization

Maintainer FreedomIntelligence · Last updated April 1, 2026

Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.

OpenClawNanoClawAnalysisReproductionbio-multi-omics-data-harmonization🧬 bioinformatics (gptomics bio-* suite)bioinformatics — multi-omics integrationpreprocessing

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-multi-omics-data-harmonization

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

R: MultiAssayExperiment for unified multi-omics containers.
Prepare my multi-omics data for integration" → Normalize, batch-correct, align features, and handle missing values across RNA-seq, proteomics, methylation, and other data types before joint analysis. R: MultiAssayExperiment for unified multi-omics containers.
rna <- SummarizedExperiment(assays = list(counts = rna_matrix), colData = sample_info) protein <- SummarizedExperiment(assays = list(intensity = protein_matrix), colData = sample_info) methylation <- SummarizedExperiment(assays = list(beta = meth_matrix), colData = sample_info).

Source Doc

Excerpt From SKILL.md

MultiAssayExperiment Structure

library(MultiAssayExperiment)

## Create experiment list

exp_list <- ExperimentList(RNA = rna, Protein = protein, Methylation = methylation)

## Sample map (links samples to assays)

smap <- data.frame(
    assay = rep(c('RNA', 'Protein', 'Methylation'), each = nrow(sample_info)),
    primary = rep(sample_info$SampleID, 3),
    colname = c(colnames(rna_matrix), colnames(protein_matrix), colnames(meth_matrix))
)