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) .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("MOSS")

0.1.0 by Agustin Gonzalez-Reymundez, 5 months ago


https://github.com/agugonrey/MOSS


Report a bug at https://github.com/agugonrey/MOSS/issues


Browse source code at https://github.com/cran/MOSS


Authors: Agustin Gonzalez-Reymundez [aut, cre] , Alexander Grueneberg [aut] , Ana Vazquez [ctb]


Documentation:   PDF Manual  


GPL-2 license


Imports cluster, dbscan, Rtsne, stats

Suggests annotate, bigparallelr, bigstatsr, clValid, ComplexHeatmap, fpc, ggplot2, ggpmisc, ggthemes, gridExtra, irlba, knitr, MASS, rmarkdown, testthat, viridis


See at CRAN