Package: MOSS 0.2.2

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 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>.

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

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NEWS

# Install 'MOSS' in R:
install.packages('MOSS', repos = c('https://agugonrey.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/agugonrey/moss/issues

On CRAN:

elastic-netmulti-omic-integrationsparse-singular-value-decompositionvariable-selection

5.55 score 7 stars 17 scripts 715 downloads 6 mentions 16 exports 5 dependencies

Last updated 9 months agofrom:17798c79f4. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 10 2024
R-4.5-winNOTEOct 10 2024
R-4.5-linuxNOTEOct 10 2024
R-4.4-winNOTEOct 10 2024
R-4.4-macNOTEOct 10 2024
R-4.3-winOKOct 10 2024
R-4.3-macOKOct 10 2024

Exports:aest.fcov_adjmetdatmossmoss_heatmapmoss_selectmoss_signaturesmoss_vennpca2tsneprepro_naprepro_subsimulate_datassvdENssvdEN_sol_pathssvdEN_sol_path_partsne2clus

Dependencies:clusterdbscangenericsRcppRtsne

MOSS: Multi-omic integration via sparse singular value decomposition

Rendered fromMOSS_working_example.Rmdusingknitr::rmarkdownon Oct 10 2024.

Last update: 2021-07-08
Started: 2020-05-20