bio-multi-omics-mixomics-analysis

Version Compatibility

Reference examples tested with: mixOmics 6.26+

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.

mixOmics Multi-Omics Analysis

"Integrate my multi-omics data with supervised analysis" → Identify cross-omics feature signatures that discriminate between groups using sparse PLS and multi-block discriminant analysis.

• R: mixOmics::block.splsda() (DIABLO), mixOmics::spls() for pairwise integration

Setup and Data Preparation

Goal: Load and align omics matrices with matching sample labels and phenotype information.

Approach: Read each omics layer and phenotype, then intersect to common samples.

library(mixOmics)

# Load omics matrices (samples x features)
X_rna <- as.matrix(read.csv('rnaseq.csv', row.names = 1))
X_protein <- as.matrix(read.csv('proteomics.csv', row.names = 1))
Y <- factor(read.csv('phenotype.csv')$Condition)

# Ensure matching samples
common <- Reduce(intersect, list(rownames(X_rna), rownames(X_protein)))
X_rna <- X_rna[common, ]
X_protein <- X_protein[common, ]
Y <- Y[match(common, read.csv('phenotype.csv')$Sample)]

Pairwise Integration: sPLS

Goal: Identify correlated features between two omics layers using sparse partial least squares.

Approach: Tune component count, fit sPLS with feature selection (keepX/keepY), and visualize cross-omics correlations.

# Sparse Partial Least Squares for two datasets
# Finds correlated features between omics

# Tune number of components
tune_spls <- perf(spls(X_rna, X_protein, ncomp = 5), validation = 'Mfold', folds = 5)
plot(tune_spls)

# Run sPLS
spls_result <- spls(X_rna, X_protein, ncomp = 3, keepX = c(50, 50, 50), keepY = c(30, 30, 30))

# Visualize correlations
plotIndiv(spls_result, comp = c(1, 2), group = Y, legend = TRUE)
plotVar(spls_result, comp = c(1, 2), var.names = TRUE)

# Correlation circle
plotArrow(spls_result, group = Y)

# Heatmap of selected features
cim(spls_result, comp = 1)