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OSNMTF  

Orthogonal Sparse Non-Negative Matrix Tri-Factorization
View on CRAN: Click here


Download and install OSNMTF package within the R console
Install from CRAN:
install.packages("OSNMTF")

Install from Github:
library("remotes")
install_github("cran/OSNMTF")

Install by package version:
library("remotes")
install_version("OSNMTF", "0.1.0")



Attach the package and use:
library("OSNMTF")
Maintained by
Xiaoyao Yin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-11-28
Latest Update: 2019-11-28
Description:
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.
How to cite:
Xiaoyao Yin (2019). OSNMTF: Orthogonal Sparse Non-Negative Matrix Tri-Factorization. R package version 0.1.0, https://cran.r-project.org/web/packages/OSNMTF. Accessed 05 Jun. 2026.
Previous versions and publish date:
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Complete documentation for OSNMTF
Functions, R codes and Examples using the OSNMTF R package
Some associated functions: ASR . MSR . OSNMTF . Standard_Normalization . affinityMatrix . cost . dist2eu . initialization . simu_data_generation . update_B . update_C . update_L . update_R . 
Some associated R codes: ASR.R . MSR.R . OSNMTF.R . Standard_Normalization.R . affinityMatrix.R . cost.R . dist2eu.R . initialization.R . simu_data_generation.R . update_B.R . update_C.R . update_L.R . update_R.R .  Full OSNMTF package functions and examples
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