Orthogonal Sparse Non-Negative Matrix Tri-Factorization
Calculate the similarity matrix
Average Residue
Calculate the cost
Euclidean Distance
initialize the values used in NMTFOSC
Mean Residue
The algorithm OSNMTF
Generate simulation data
Standard Normalization
Update sub-matrix B
Update sub-matrix C
Update sub-matrix L
Update sub-matrix R
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.