MOSS: Multi-Omic Integration via Sparse Singular Value Decomposition

High dimensionality, noise and heterogeneity among samples and features challenge the omic integration task. Here we present an omic integration method based on sparse singular value decomposition (SVD) to deal with these limitations, by: a. obtaining the main axes of variation of the combined omics, b. imposing sparsity constraints at both subjects (rows) and features (columns) levels using Elastic Net type of shrinkage, and d. allowing both linear and non-linear projections (via t-Stochastic Neighbor Embedding) of the omic data to detect clusters in very convoluted data (Gonzalez-Reymundez & Vazquez, 2020) <doi:10.1038/s41598-020-65119-5>.

Version: 0.1.0
Imports: cluster, dbscan, Rtsne, stats
Suggests: annotate, bigparallelr, bigstatsr, clValid, ComplexHeatmap, fpc, ggplot2, ggpmisc, ggthemes, gridExtra, irlba, knitr, MASS, rmarkdown, testthat, viridis
Published: 2021-01-19
Author: Agustin Gonzalez-Reymundez [aut, cre], Alexander Grueneberg [aut], Ana Vazquez [ctb]
Maintainer: Agustin Gonzalez-Reymundez <agugonrey at>
License: GPL-2
NeedsCompilation: no
Materials: README
CRAN checks: MOSS results


Reference manual: MOSS.pdf
Vignettes: <<<<Vignette Title>>>>
Package source: MOSS_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: MOSS_0.1.0.tgz, r-oldrel: MOSS_0.1.0.tgz


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