OSNMTF: Orthogonal Sparse Non-Negative Matrix Tri-Factorization

A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization. To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri- Factorization" which will be submitted to BBRC.

Version: 0.1.0
Depends: R (≥ 3.4.4)
Imports: dplyr, MASS, stats
Suggests: knitr, rmarkdown
Published: 2019-11-28
DOI: 10.32614/CRAN.package.OSNMTF
Author: Xiaoyao Yin
Maintainer: Xiaoyao Yin <yinxy1992 at sina.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: OSNMTF results


Reference manual: OSNMTF.pdf
Vignettes: An Introduction to the package OSNMTF


Package source: OSNMTF_0.1.0.tar.gz
Windows binaries: r-devel: OSNMTF_0.1.0.zip, r-release: OSNMTF_0.1.0.zip, r-oldrel: OSNMTF_0.1.0.zip
macOS binaries: r-release (arm64): OSNMTF_0.1.0.tgz, r-oldrel (arm64): OSNMTF_0.1.0.tgz, r-release (x86_64): OSNMTF_0.1.0.tgz, r-oldrel (x86_64): OSNMTF_0.1.0.tgz


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