Agent skill

bio-multi-omics-data-harmonization

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.

View SKILL.md on GitHub Repository
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npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-multi-omics-data-harmonization

SKILL.md

Version Compatibility

Reference examples tested with: DESeq2 1.42+

Before using code patterns, verify installed versions match. If versions differ:

  • R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Data Harmonization for Multi-Omics

"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

MultiAssayExperiment Structure

r
library(MultiAssayExperiment)

# Load individual assays
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)

# 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))
)

# Create MAE
mae <- MultiAssayExperiment(experiments = exp_list, colData = sample_info, sampleMap = smap)

Normalization Per Assay

r
# RNA-seq: VST normalization
library(DESeq2)
dds <- DESeqDataSetFromMatrix(countData = assay(mae, 'RNA'),
                               colData = colData(mae),
                               design = ~ 1)
vst_rna <- assay(vst(dds))

# Proteomics: Log2 + median centering
log2_protein <- log2(assay(mae, 'Protein'))
log2_protein[is.infinite(log2_protein)] <- NA
medians <- apply(log2_protein, 2, median, na.rm = TRUE)
norm_protein <- sweep(log2_protein, 2, medians - median(medians))

# Methylation: M-value transformation
beta <- assay(mae, 'Methylation')
m_values <- log2(beta / (1 - beta))

Cross-Omics Batch Correction

Goal: Remove batch effects across multi-omics data types while preserving biological signal from condition differences.

Approach: Stack normalized matrices from RNA, protein, and methylation assays for common samples, apply ComBat batch correction on the combined matrix, then split back into per-assay corrected matrices.

r
library(sva)

# Combine normalized matrices for joint batch correction
# Only use common samples
common_samples <- Reduce(intersect, colnames(mae))

combined <- rbind(
    vst_rna[, common_samples],
    norm_protein[, common_samples],
    m_values[, common_samples]
)

# Add omics type as covariate
omics_type <- c(rep('RNA', nrow(vst_rna)),
                rep('Protein', nrow(norm_protein)),
                rep('Methylation', nrow(m_values)))

# ComBat for batch correction
batch <- colData(mae)[common_samples, 'Batch']
mod <- model.matrix(~ Condition, data = colData(mae)[common_samples, ])

corrected <- ComBat(dat = combined, batch = batch, mod = mod)

# Split back into separate matrices
idx_rna <- 1:nrow(vst_rna)
idx_prot <- (nrow(vst_rna) + 1):(nrow(vst_rna) + nrow(norm_protein))
idx_meth <- (nrow(vst_rna) + nrow(norm_protein) + 1):nrow(combined)

corrected_rna <- corrected[idx_rna, ]
corrected_protein <- corrected[idx_prot, ]
corrected_meth <- corrected[idx_meth, ]

Feature Alignment (Gene-Level)

r
library(biomaRt)

# Map protein IDs to gene symbols
ensembl <- useEnsembl(biomart = 'genes', dataset = 'hsapiens_gene_ensembl')

# Protein to gene mapping
protein_ids <- rownames(norm_protein)
protein_mapping <- getBM(attributes = c('uniprotswissprot', 'hgnc_symbol'),
                          filters = 'uniprotswissprot',
                          values = protein_ids,
                          mart = ensembl)

# Aggregate proteins to gene level (mean)
protein_gene <- norm_protein
rownames(protein_gene) <- protein_mapping$hgnc_symbol[match(rownames(protein_gene), protein_mapping$uniprotswissprot)]
protein_gene <- protein_gene[!is.na(rownames(protein_gene)), ]
protein_gene <- aggregate(. ~ rownames(protein_gene), data = as.data.frame(protein_gene), FUN = mean)

# Map methylation probes to genes
# (requires annotation package, e.g., IlluminaHumanMethylation450kanno.ilmn12.hg19)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
anno <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)
probe_genes <- anno[rownames(m_values), 'UCSC_RefGene_Name']

Missing Value Handling

r
# Per-assay missing value analysis
missing_summary <- function(mat) {
    data.frame(
        total_missing = sum(is.na(mat)),
        pct_missing = mean(is.na(mat)) * 100,
        samples_complete = sum(colSums(is.na(mat)) == 0),
        features_complete = sum(rowSums(is.na(mat)) == 0)
    )
}

lapply(list(RNA = vst_rna, Protein = norm_protein, Methylation = m_values), missing_summary)

# Filter features with too many missing values
filter_missing <- function(mat, max_missing_pct = 50) {
    keep <- rowMeans(is.na(mat)) * 100 < max_missing_pct
    mat[keep, ]
}

protein_filtered <- filter_missing(norm_protein, max_missing_pct = 30)

# Imputation (MinProb for proteomics)
impute_minprob <- function(mat) {
    for (i in 1:ncol(mat)) {
        nas <- is.na(mat[, i])
        if (any(nas)) {
            q01 <- quantile(mat[, i], 0.01, na.rm = TRUE)
            mat[nas, i] <- rnorm(sum(nas), mean = q01, sd = abs(q01) * 0.1)
        }
    }
    mat
}

protein_imputed <- impute_minprob(protein_filtered)

Sample Matching and Subsetting

r
# Find complete samples across all assays
complete_samples <- intersectColumns(mae)
cat('Samples in all assays:', ncol(complete_samples), '\n')

# Subset to common samples
mae_matched <- mae[, complete_samples, ]

# Alternative: keep samples with N-1 assays
subsetByColData(mae, mae$has_at_least_2_assays)

Scale and Center

r
# Z-score transformation (per feature)
scale_matrix <- function(mat) {
    t(scale(t(mat)))
}

scaled_rna <- scale_matrix(vst_rna)
scaled_protein <- scale_matrix(norm_protein)
scaled_meth <- scale_matrix(m_values)

# Verify scaling
cat('RNA mean:', mean(scaled_rna, na.rm = TRUE), 'sd:', sd(scaled_rna, na.rm = TRUE), '\n')

Export Harmonized Data

r
# Save as list for integration tools
harmonized <- list(
    RNA = scaled_rna,
    Protein = scaled_protein,
    Methylation = scaled_meth,
    sample_info = colData(mae)[common_samples, ]
)

saveRDS(harmonized, 'harmonized_multiomics.rds')

# Or as separate CSVs
write.csv(scaled_rna, 'harmonized_rna.csv')
write.csv(scaled_protein, 'harmonized_protein.csv')
write.csv(scaled_meth, 'harmonized_methylation.csv')

Related Skills

  • mofa-integration - Use harmonized data in MOFA2
  • mixomics-analysis - Use harmonized data in mixOmics
  • differential-expression/batch-correction - RNA-seq batch correction
  • proteomics/proteomics-qc - Proteomics-specific QC

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