Description
Multi-Omic Integration via Sparse Singular Value Decomposition.
Description
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 c. 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 et. al, 2022) <doi:10.1093/bioinformatics/btac179>.
README.md
MOSS: Multi-omic integration via sparse singular value decomposition.
Agustin Gonzalez-Reymundez, Alexander Grueneberg, and Ana I. Vazquez.
Installing and loading MOSS from CRAN.
install.packages("MOSS")
library("MOSS")
Installing and loading MOSS from GitHub.
if (require("remotes") == FALSE) install.packages("remotes")
install_github("agugonrey/MOSS")
library("MOSS")
Article
MOSS: Multi-omic integration with Sparse Value Decomposition
Documentation.
For a description of the package's main function.
help(moss)
For more documentation, see:
supplementary information for Gonzalez-Reymundez et al (2022)
and