bio-multi-omics-data-harmonization

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Summary

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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

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

# 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))

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, ]

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']

# 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)

# 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)

# 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')

# 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')

bio-multi-omics-data-harmonization

Summary

SKILL.md

Version Compatibility

Data Harmonization for Multi-Omics

MultiAssayExperiment Structure

Normalization Per Assay

Cross-Omics Batch Correction

Feature Alignment (Gene-Level)

Missing Value Handling

Sample Matching and Subsetting

Scale and Center

Export Harmonized Data

Related Skills

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