bio-multi-omics-data-harmonization

name	bio-multi-omics-data-harmonization
description	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.
tool_type	r
primary_tool	MultiAssayExperiment

Data Harmonization for Multi-Omics

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 - medianmedians


beta  assaymae 
m_values  log2beta    beta

Cross-Omics Batch Correction

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 <- colDatamaecommon_samples 
mod  model.matrix Condition data  colDatamaecommon_samples 

corrected  ComBatdat  combined batch  batch mod  mod


idx_rna  1nrowvst_rna
idx_prot  nrowvst_rna  nrowvst_rna  nrownorm_protein
idx_meth  nrowvst_rna  nrownorm_protein  nrowcombined

corrected_rna  correctedidx_rna 
corrected_protein  correctedidx_prot 
corrected_meth  correctedidx_meth 

Feature Alignment (Gene-Level)

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.narownamesprotein_gene 
protein_gene  aggregate.  rownamesprotein_gene data  as.data.frameprotein_gene FUN  mean



libraryIlluminaHumanMethylation450kanno.ilmn12.hg19
anno  getAnnotationIlluminaHumanMethylation450kanno.ilmn12.hg19
probe_genes  annorownamesm_values 

Missing Value Handling

# 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_missing  mat max_missing_pct   
    keep  rowMeansmat    max_missing_pct
    matkeep 


protein_filtered  filter_missingnorm_protein max_missing_pct  


impute_minprob  mat 
     i  ncolmat 
        nas  mat i
         nas 
            q01  quantilemat i  na.rm  
            matnas i  rnormnas mean  q01 sd  q01  
        
    
    mat


protein_imputed  impute_minprobprotein_filtered

Sample Matching and Subsetting

# 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

# 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

# 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