Version Compatibility

Reference examples tested with: MOFA2 1.12+, mixOmics 6.26+, SNFtool 2.3+, clusterProfiler 4.10+, ggplot2 3.5+

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.

Multi-omics Integration Pipeline

"Integrate my multi-omics datasets" -> Decide the strategy first, then orchestrate harmonization, the chosen integration method (MOFA2, mixOmics, or SNF), interpretation, and validation - because bulk multi-omics is small-n, huge-p, so an unvalidated integrated result is the default noise outcome.

Before any tool runs, settle the design with multi-omics-integration/integration-design: confirm the data is vertical (different omics on the SAME samples, not the same features across cohorts), map the question to a method (shared factors -> MOFA2, predictive signature -> DIABLO, patient subtypes -> SNF), and plan a held-out cohort because in-cohort cross-validation at small n is optimistically biased. Inspect the per-view variance-explained table to confirm no single omic dominates the shared structure.

The governing principle

Bulk multi-omics is small-n, huge-p, so an UNVALIDATED integrated result is the DEFAULT noise outcome, not the exception. Every honesty decision is made at a seam before the integrator runs.

The sample-matching key must be VERTICAL, and joined on a stable primary key. Vertical = different features, SAME samples (what this category is FOR); horizontal (same features, different cohorts) is meta-analysis/batch, a different problem — conflating them is the deepest category error. The shared latent factor is indexed by SAMPLE, so establish the subject primary key ONCE and externalize it (MAE sampleMap / consistent MuData obs_names); NEVER cbind/pd.concat on assumed row order (silently mis-pairs subjects). Gene SYMBOLS are display labels, not join keys (MARCH1->MARCHF1, Excel corruption) — join on stable Ensembl IDs.
Each omic is normalized in its OWN space FIRST, then block variance is equalized BEFORE stacking. A shared-latent integrator decomposes total variance and assumes each feature is ~continuous/Gaussian; the non-negotiable per-omic transforms (RNA-seq -> VST/logCPM never raw counts; methylation -> M-values; prot/metab -> log2 + MNAR-aware; microbiome -> CLR) come first, then per-VIEW scaling (MOFA scale_views=TRUE) equalizes each block's CONTRIBUTION. Variance is additive across features, so a block with more features (850k CpGs vs 100 metabolites) casts more votes and hijacks the shared space regardless of biological importance. Both failures are silent.
No factor/subtype/signature is credible until it reproduces in a HELD-OUT cohort. In-cohort CV at n<<p is optimistically biased (tuning feature selection on the full data then reporting same-data CV inflates AUC by up to ~0.15); the gold standard is an external test set or fully nested CV. Spurious cross-omic correlation is the DEFAULT at p>>n. Batch is corrected ONCE, in ONE place (ComBat each omic AND modeling batch downstream removes it twice and over-shrinks real biology).

The single best honesty check: if every shared factor is dominated by one view, the pipeline did not integrate — it re-discovered the biggest omic.

Made-once commitments

Commitment	Consequence inherited downstream
Correspondence axis (VERTICAL, not horizontal)	Whether the integrator's math has anything to align on; conflating the two is the deepest category error
Sample primary key (externalized in MAE/MuData)	Correct subject-to-assay linkage; cbind-on-row-order silently mis-pairs subjects
Per-block normalization + per-view variance scaling	Whether the widest omic hijacks every factor; done before stacking, never after
Validation plan (held-out cohort)	Whether any factor/signature is credible; in-cohort CV at n<<p is optimistically biased

Pipeline Overview

RNA-seq Data ─────┐
                  │
Proteomics Data ──┼──> Data Harmonization ──> Integration ──> Factors/Components
                  │                                                    │
Metabolomics ─────┘                                                    ▼
                        ┌─────────────────────────────────────────────────────┐
                        │           multi-omics-pipeline                      │
                        ├─────────────────────────────────────────────────────┤
                        │  1. Data Preprocessing per Modality                 │
                        │  2. Sample Harmonization (matching samples)         │
                        │  3. Feature Selection/Filtering                     │
                        │  4. Integration (MOFA2 / mixOmics / SNF)            │
                        │  5. Factor/Component Interpretation                 │
                        │  6. Downstream Analysis                             │
                        └─────────────────────────────────────────────────────┘
                                                                       │
                                                                       ▼
                              Integrated Factors + Biomarker Signatures

Complete MOFA2 Workflow

Goal: Discover unsupervised shared and view-specific factors across the harmonized omics, then interpret and validate them.

Approach: Harmonize to common samples, feature-select per view, train MOFA2, read the per-view variance decomposition first, label factors against biological and technical covariates, and run enrichment on the signed weights.

library(MOFA2)
library(MOFAdata)
library(ggplot2)
library(tidyverse)

# === 1. LOAD AND HARMONIZE DATA ===
# RNA-seq data (samples x genes)
rna <- read.csv('rnaseq_normalized.csv', row.names = 1)
cat('RNA:', nrow(rna), 'samples,', ncol(rna), 'genes\n')

# Proteomics data (samples x proteins)
protein <- read.csv('proteomics_normalized.csv', row.names = 1)
cat('Protein:', nrow(protein), 'samples,', ncol(protein), 'proteins\n')

# Metabolomics data (samples x metabolites)
metab <- read.csv('metabolomics_normalized.csv', row.names = 1)
cat('Metabolites:', nrow(metab), 'samples,', ncol(metab), 'metabolites\n')

# Find common samples
common_samples <- Reduce(intersect, list(rownames(rna), rownames(protein), rownames(metab)))
cat('Common samples:', length(common_samples), '\n')

# Subset to common samples
rna <- rna[common_samples, ]
protein <- protein[common_samples, ]
metab <- metab[common_samples, ]

# === 2. FEATURE SELECTION ===
# Select most variable features per modality
select_variable <- function(data, n = 2000) {
    vars <- apply(data, 2, var, na.rm = TRUE)
    top_features <- names(sort(vars, decreasing = TRUE))[1:min(n, ncol(data))]
    data[, top_features]
}

rna_var <- select_variable(rna, n = 2000)
protein_var <- select_variable(protein, n = 1000)
metab_var <- select_variable(metab, n = 500)

# === 3. CREATE MOFA OBJECT ===
# Prepare data as list of matrices (features x samples)
data_list <- list(
    RNA = t(as.matrix(rna_var)),
    Protein = t(as.matrix(protein_var)),
    Metabolome = t(as.matrix(metab_var))
)

# Create MOFA object
mofa <- create_mofa(data_list)

# Add sample metadata (samples_metadata<- requires a literal 'sample' column)
sample_metadata <- read.csv('sample_metadata.csv')
rownames(sample_metadata) <- sample_metadata$sample_id
sample_metadata$sample <- sample_metadata$sample_id
samples_metadata(mofa) <- sample_metadata[common_samples, ]

# === 4. CONFIGURE AND TRAIN MODEL ===
# Data options
data_opts <- get_default_data_options(mofa)
data_opts$scale_views <- TRUE  # Scale each view

# Model options
model_opts <- get_default_model_options(mofa)
model_opts$num_factors <- 15  # Number of factors to learn

# Training options
train_opts <- get_default_training_options(mofa)
train_opts$maxiter <- 1000
train_opts$convergence_mode <- 'slow'
train_opts$seed <- 42

# Prepare and train
mofa <- prepare_mofa(mofa, data_options = data_opts,
                      model_options = model_opts,
                      training_options = train_opts)

cat('Training MOFA model...\n')
mofa <- run_mofa(mofa, outfile = 'mofa_model.hdf5', use_basilisk = TRUE)

# === 5. ANALYZE FACTORS ===
# Variance explained per factor per view
plot_variance_explained(mofa, max_r2 = 15)
ggsave('variance_explained.png', width = 10, height = 6)

# Factor values
factor_values <- get_factors(mofa)[[1]]

# Correlate factors with biological AND technical covariates; a factor that tracks batch is a batch factor
correlate_factors_with_covariates(mofa, covariates = c('condition', 'batch', 'depth'))

# Factor plots
plot_factor(mofa, factors = 1:4, color_by = 'condition', dot_size = 3)
ggsave('factor_scatter.png', width = 12, height = 10)

# === 6. INTERPRET FACTORS ===
# Get top weights per factor per view
for (f in 1:5) {
    cat('\nFactor', f, ':\n')
    weights <- get_weights(mofa, factors = f, as.data.frame = TRUE)

    for (view in unique(weights$view)) {
        view_weights <- weights[weights$view == view, ]
        view_weights <- view_weights[order(abs(view_weights$value), decreasing = TRUE), ]
        cat('  ', view, ':', paste(head(view_weights$feature, 5), collapse = ', '), '\n')
    }
}

# Heatmap of top features per factor
plot_top_weights(mofa, view = 'RNA', factors = 1:5, nfeatures = 10)
ggsave('top_weights_rna.png', width = 10, height = 8)

# === 7. ENRICHMENT ANALYSIS ===
library(clusterProfiler)
library(org.Hs.eg.db)

# Get RNA weights for factor 1
rna_weights <- get_weights(mofa, views = 'RNA', factors = 1)[[1]][, 1]
top_genes <- names(sort(abs(rna_weights), decreasing = TRUE))[1:200]

# GO enrichment -- use all RNA features as background (not the full genome)
all_rna_genes <- names(rna_weights)
ego <- enrichGO(gene = top_genes,
                universe = all_rna_genes,
                OrgDb = org.Hs.eg.db,
                keyType = 'SYMBOL',
                ont = 'BP',
                pvalueCutoff = 0.05)
ego <- simplify(ego, cutoff = 0.7, by = 'p.adjust')

dotplot(ego, showCategory = 15)
ggsave('factor1_enrichment.png', width = 8, height = 10)

# === 8. DOWNSTREAM: SURVIVAL ANALYSIS ===
library(survival)
library(survminer)

# Add factor values to metadata
surv_data <- data.frame(
    sample = rownames(factor_values),
    factor1 = factor_values[, 1],
    time = sample_metadata[rownames(factor_values), 'survival_time'],
    status = sample_metadata[rownames(factor_values), 'survival_status']
)

# Median split
surv_data$factor1_group <- ifelse(surv_data$factor1 > median(surv_data$factor1), 'High', 'Low')

# Kaplan-Meier
fit <- survfit(Surv(time, status) ~ factor1_group, data = surv_data)
ggsurvplot(fit, data = surv_data, pval = TRUE, risk.table = TRUE)
ggsave('survival_factor1.png', width = 8, height = 8)

# === 9. EXPORT RESULTS ===
# Factor values
write.csv(factor_values, 'mofa_factor_values.csv')

# Weights
all_weights <- get_weights(mofa, as.data.frame = TRUE)
write.csv(all_weights, 'mofa_weights.csv', row.names = FALSE)

cat('\nMOFA analysis complete!\n')

mixOmics DIABLO Workflow

Goal: Build a supervised cross-omic signature that discriminates a known outcome, with an honest performance estimate.

Approach: Set the design matrix from the goal, tune the component count then keepX inside cross-validation folds with balanced error rate, fit, and report performance from data not used in tuning.

library(mixOmics)

# === 1. PREPARE DATA ===
# Same preprocessing as above
X <- list(
    RNA = as.matrix(rna_var),
    Protein = as.matrix(protein_var),
    Metabolome = as.matrix(metab_var)
)

# Outcome variable
Y <- factor(sample_metadata[common_samples, 'condition'])

# === 2. DESIGN MATRIX (the central DIABLO decision) ===
# off-diagonal trades discrimination vs cross-block correlation: ~1 for a coherent network,
# <0.5 for prediction. 0.1 leans toward prediction and is tutorial convention, not a default.
design <- matrix(0.5, ncol = length(X), nrow = length(X),
                 dimnames = list(names(X), names(X)))
diag(design) <- 0

# === 3. TUNE MODEL ===
# Tune number of components
perf.diablo <- perf(block.splsda(X, Y, ncomp = 5, design = design),
                    validation = 'Mfold', folds = 5, nrepeat = 10)

ncomp <- perf.diablo$choice.ncomp$WeightedVote['Overall.BER', 'max.dist']
cat('Optimal components:', ncomp, '\n')

# Tune number of variables per component
test.keepX <- list(
    RNA = c(10, 25, 50, 100),
    Protein = c(5, 10, 25, 50),
    Metabolome = c(5, 10, 25)
)

tune.diablo <- tune.block.splsda(X, Y, ncomp = ncomp, test.keepX = test.keepX,
                                  design = design, validation = 'Mfold', folds = 5)

optimal.keepX <- tune.diablo$choice.keepX

# === 4. FINAL MODEL ===
diablo.model <- block.splsda(X, Y, ncomp = ncomp,
                              keepX = optimal.keepX, design = design)

# === 5. VISUALIZATION ===
# Sample plot
plotIndiv(diablo.model, ind.names = FALSE, legend = TRUE, title = 'DIABLO Sample Plot')

# Variable plot
plotVar(diablo.model, var.names = FALSE, style = 'graphics', legend = TRUE)

# Circos plot
circosPlot(diablo.model, cutoff = 0.7, line = TRUE,
           color.blocks = c('darkorchid', 'brown1', 'lightgreen'))

# Heatmap
cimDiablo(diablo.model, color.blocks = c('darkorchid', 'brown1', 'lightgreen'),
          margins = c(10, 5))

# === 6. PERFORMANCE (report from data not used to tune; an external test set is the honest estimate) ===
perf.final <- perf(diablo.model, validation = 'Mfold', folds = 5, nrepeat = 10)
perf.final$WeightedVote.error.rate   # matrix: classes + Overall.BER by component

# ROC curves
auc.diablo <- auroc(diablo.model, roc.block = 'RNA', roc.comp = 1)

Similarity Network Fusion (SNF)

Goal: Stratify patients into candidate subtypes from the fused multi-omic similarity network.

Approach: Standardize each omic, build local-scaled affinity networks, fuse by cross-diffusion, estimate a plausible cluster number, and defend it with a fused-versus-single-omic concordance check before claiming subtypes.

library(SNFtool)

# === 1. CREATE SIMILARITY MATRICES ===
K <- 20       # neighbors for the local kernel bandwidth (10-30)
sigma <- 0.5  # affinityMatrix width (the arg is sigma, not alpha); 0.3-0.8

# standardize per feature, then squared-Euclidean -> root -> local-scaled kernel
views <- lapply(list(rna_var, protein_var, metab_var), function(x) standardNormalization(as.matrix(x)))
affinities <- lapply(views, function(x) affinityMatrix(dist2(x, x)^(1/2), K, sigma))   # dist2 returns SQUARED distance

# === 2. FUSE NETWORKS ===
W <- SNF(affinities, K, t = 20)

# === 3. CLUSTER ON FUSED NETWORK (defend the count, do not assume it) ===
estimateNumberOfClustersGivenGraph(W, NUMC = 2:8)   # four eigengap/rotation estimates - plausibility, not truth
clusters <- spectralClustering(W, K = 3)            # here K is the CLUSTER COUNT
concordanceNetworkNMI(c(affinities, list(W)), 3)    # did fusion beat the best single omic? (Rappoport and Shamir 2018)

# === 4. VISUALIZATION ===
# Plot fused network
displayClustersWithHeatmap(W, clusters)

QC Checkpoints

Stage	Check	Action if Failed
Sample matching	>80% samples shared	Check sample IDs
Missing values	<20% per modality	Impute or remove
Feature variance	Features vary	Filter low variance
Model convergence	ELBO plateau	Increase iterations
Factor variance	drop factors below ~1-2% in all views	set dropfactorthreshold; keep fewer factors
Variance imbalance	no single view dominates every factor	per-view scaling or filter the wider view harder
Validation	held-out cohort, not in-cohort CV	replicate before claiming a biomarker/subtype

Workflow Variants

With Missing Samples

# MOFA2 handles missing views gracefully; create_mofa_from_df wants one row per (sample, feature, value)
to_long <- function(mat, view) {
    df <- as.data.frame(as.table(as.matrix(mat)))   # samples x features -> Var1=sample, Var2=feature, Freq=value (alignment preserved)
    data.frame(sample = as.character(df$Var1), feature = as.character(df$Var2), view = view, value = df$Freq)
}
data_long <- rbind(to_long(rna, 'RNA'), to_long(protein, 'Protein'))
mofa <- create_mofa_from_df(data_long)

Single-cell Multi-omics

Single-cell multimodal data (CITE-seq, 10x Multiome) is a different paradigm - per-cell generative models with abundant observations rather than the bulk small-n regime. Route it to single-cell/multimodal-integration rather than applying this bulk pipeline.

Common Errors

Symptom	Cause	Fix
Integrated result on mislabeled subjects (both axes have plausible lengths)	`cbind`/`pd.concat` on assumed sample order	Enforce sample linkage via a container (MAE `sampleMap` / MuData `intersect_obs`); assert shape after every cross-language hop
One view dominates every factor	Block scale/feature-count imbalance (850k CpGs vs 100 metabolites)	Per-VIEW scaling (`scale_views=TRUE`), NOT per-feature; filter larger views harder; check per-(factor,view) R2
A near-constant noise feature blown up to variance 1	Per-feature scaling (`scale=TRUE` in mixOmics)	Aggressive low-variance filtering BEFORE scaling; prefer per-view scaling
A "biological" factor that is really batch	Technical variance not regressed out	Correlate every factor with technical covariates (run date, plate, site); exclude a factor tracking batch more than biology
AUC inflated by up to ~0.15	In-cohort CV with feature selection outside the folds	CV must WRAP selection (nested); gold standard is an external test set (`predict()`/`auroc()`)
Batch removed twice, real biology over-shrunk	ComBat each omic AND batch in the downstream model	Correct batch ONCE: pre-correct and do not re-enter, OR leave raw and model batch as a covariate
Transposed matrices make every gene a "sample"	Bioconductor (samples=cols) vs mixOmics/MOFA (samples=rows) mismatch	Transpose deliberately + assert shape on every cross-package hop

References

Argelaguet R, Velten B, Arnol D, et al (2018) Multi-Omics Factor Analysis - a framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology 14:e8124. DOI 10.15252/msb.20178124. (MOFA.)
Rohart F, Gautier B, Singh A, Le Cao KA (2017) mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Computational Biology 13:e1005752. DOI 10.1371/journal.pcbi.1005752.
Singh A, Shannon CP, Gautier B, et al (2019) DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35:3055-3062. DOI 10.1093/bioinformatics/bty1054.
Wang B, Mezlini AM, Demir F, et al (2014) Similarity network fusion for aggregating data types on a genomic scale. Nature Methods 11:333-337. DOI 10.1038/nmeth.2810. (SNF.)
Rappoport N, Shamir R (2018) Multi-omic and multi-view clustering algorithms: review and cancer benchmark. Nucleic Acids Research 46:10546-10562. DOI 10.1093/nar/gky889. (integration is not automatically better than the best single omic.)
Nygaard V, Rodland EA, Hovig E (2016) Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics 17:29-39. DOI 10.1093/biostatistics/kxv027.

Related Skills

multi-omics-integration/integration-design - Method selection, correspondence, and the n<<p discipline (decide first)
multi-omics-integration/mofa-integration - MOFA2 unsupervised factor analysis
multi-omics-integration/mixomics-analysis - mixOmics DIABLO/sPLS/MINT methods
multi-omics-integration/similarity-network - SNF patient stratification
multi-omics-integration/data-harmonization - Cross-omic preprocessing and scaling
pathway-analysis/go-enrichment - Factor/signature interpretation
differential-expression/batch-correction - Batch effects
clinical-biostatistics/survival-analysis - Survival validation of factors and subtypes
single-cell/multimodal-integration - Single-cell multimodal integration (different paradigm)

bio-workflows-multi-omics-pipeline

Installation

Summary

Similar popular skills

Also in this package

Agent compatibility

Package contents

History

SKILL.md