数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-multi-omics-data-harmonization

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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.

OpenClawNanoClaw分析处理复现实验bio-multi-omics-data-harmonization🧬 bioinformatics (gptomics bio-* suite)bioinformatics — multi-omics integrationpreprocessing

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

R：MultiAssayExperiment ，用于 unified multi-omics containers。
Prepare my multi-omics data ，用于 integration" → Normalize，batch-correct，align features，、 handle missing values across RNA-seq，proteomics，methylation，、 other data types before joint analysis. R：MultiAssayExperiment ，用于 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)。

原始文档

SKILL.md 摘录

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))
)