Bioconductor Mixomics

mixOmics

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 6.36.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: MASS, lattice, ggplot2
• Imports: igraph, ellipse, corpcor, RColorBrewer, dplyr, tidyr, reshape2, matrixStats, rARPACK, gridExtra, ggrepel, BiocParallel, rgl, rlang
• Install: BiocManager::install("mixOmics")

When to Use

• Performing unsupervised exploratory analysis and dimensionality reduction using pca and spca.
• Integrating multiple omics datasets measured on the same samples (N-integration) using DIABLO.
• Conducting supervised classification and feature selection for multi-class problems using splsda.

When NOT to Use

• For simple univariate differential expression, use limma or DESeq2 instead because mixOmics is designed for multivariate feature selection and integration.
• For raw mass spectrometry or sequencing read preprocessing, use xcms or edgeR for normalization, as mixOmics requires pre-processed, normalized continuous matrices.

Data Requirements

• Input: Numeric data matrix or data frames with $N$ observations (samples) in rows and $P$ predictors (variables) in columns.
• Pre-filtering: Data should be pre-filtered (e.g., < 10K predictors) to remove near-zero variance predictors and reduce computational time.
• Normalization: Data must be normalized prior to analysis (e.g., log-transformed for transcriptomics, CLR-transformed for compositional microbiome data).

Key Parameters

• ncomp (none): The number of principal components or latent dimensions to extract in pca, spca, or splsda.
• scale (TRUE): Logical indicating whether to scale variables to unit variance before analysis in pca or tune.pca.
• center (TRUE): Logical indicating whether to center variables to zero mean in pca.
• keepX (none): A numeric vector specifying the number of variables to select on each component in sparse methods like spca.
• comp (none): Specifies which components to plot in plotIndiv or plotVar (e.g., c(1, 2)).
• group (none): Factor vector used to color samples by class in plotIndiv or biplot.

Best Practices

• Use tune.pca to evaluate the cumulative proportion of explained variance and choose the optimal ncomp via a screeplot.
• Center and scale the data in pca to ensure variables with large variances do not dominate the components.
• Use selectVar to extract and rank the most important variables contributing to each component.
• Visualize sample clustering with plotIndiv and variable correlations with plotVar (correlation circle plot).

Common Pitfalls

• Misinterpreting correlation circle plots on unscaled data: If data is not scaled, cosine angles do not accurately reflect correlations; fix this by ensuring scale = TRUE is set in the PCA/PLS method.
• Overfitting in supervised models: Using PLS-DA without cross-validation can yield perfect separation by chance; fix this by using cross-validation functions like perf and tune.splsda to objectively select ncomp and keepX.
• Incorrect data orientation: mixOmics expects samples in rows and variables in columns; fix this by transposing the matrix if samples are in columns.

Alternatives

• MOFA2: Differs by using Bayesian Group Factor Analysis for multi-omics integration, handling missing values natively.
• RGCCA: Differs by focusing on regularized generalized canonical correlation analysis, which forms the theoretical basis for some mixOmics methods but offers different tuning.

Citations

• Rohart F, Gautier B, Singh A, Lê Cao KA (2017). "mixOmics: An R package for 'omics feature selection and multiple data integration." PLOS Computational Biology, 13(11): e1005752.
• Singh A, Shannon CP, Gautier B, Rohart F, Vacher M, Tebbutt SJ, Lê Cao KA (2019). "DIABLO: an integrative, multi-omics, multivariate method for multi-group classification." Bioinformatics, 35(17): 3055-3062.

References

• Homepage: http://www.mixOmics.org
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/mixOmics/inst/doc/vignettes.html

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