Multi-Omic Integration via Sparse Singular Value Decomposition
c(
person("Agustin", "Gonzalez-Reymundez", email = "agugonrey@gmail.com", role = c("aut","cre")),
person("Alexander", "Grueneberg", email = "cran@agrueneberg.info", role = c("aut")),
person("Ana", "Vazquez", email = "avazquez@msu.edu", role = c("ctb")))
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>.
annotate, bigparallelr, bigstatsr, clValid, ComplexHeatmap,
fpc, ggplot2, ggpmisc, ggthemes, gridExtra, irlba, knitr, MASS,
rmarkdown, testthat, viridis
Agustin Gonzalez-Reymundez [aut, cre],
Alexander Grueneberg [aut],
Ana Vazquez [ctb]

c(
person("Agustin", "Gonzalez-Reymundez", email = "agugonrey@gmail.com", role = c("aut","cre","cph")),
person("Alexander", "Grueneberg", email = "cran@agrueneberg.info", role = c("aut")),
person("Ana", "Vazquez", email = "avazquez@msu.edu", role = c("ctb","ths")))
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 & Vazquez, 2020) <doi:10.1038/s41598-020-65119-5>.
annotate, bigparallelr, bigstatsr, future.apply, scatterpie,
clValid, ComplexHeatmap, fpc, ggplot2, ggpmisc, ggthemes,
gridExtra, irlba, knitr, MASS, rmarkdown, testthat, viridis,
spelling
Agustin Gonzalez-Reymundez [aut, cre, cph],
Alexander Grueneberg [aut],
Ana Vazquez [ctb, ths]