nipalsMCIA : flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares
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Summary
With 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.
