ArticleBioinformatics (Oxford, England)2024
nipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- tidyexposomics: integrated exposure-omics analysis powered by tidy principles.Bioinformatics advances · 2026Article
- Putting computational models of immunity to the test-An invited challenge to predict B.pertussis vaccination responses.PLoS computational biology · 2025Article
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Abstract
summaryWith the increased reliance on multi-omics data for bulk and single-cell analyses, the availability of robust approaches to perform unsupervised learning for clustering, visualization, and feature selection is imperative. We introduce nipalsMCIA, an implementation of multiple co-inertia analysis (MCIA) for joint dimensionality reduction that solves the objective function using an extension to Nonlinear Iterative Partial Least Squares. We applied nipalsMCIA to both bulk and single-cell datasets and observed significant speed-up over other implementations for data with a large sample size and/or feature dimension. AVAILABILITY AND IMPLEMENTATION: nipalsMCIA is available as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes.
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