Here is a new tool for Gene Ranking and differential expression analysis of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization (DNMF).
1. What can DNMF do?
Gene ranks of RNA-seq for gene set enrichment analysis (GSEA) due to there are no built-in ranking methods for RNA-seq data. Or other tools requiring gene ranks. Additionally, differential expression analysis of RNA-Seq Data
2. Why DNMF?
Please read Jia Z, Zhang X, Guan N, Bo X, Barnes MR, Luo Z (2015) Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization. PLoS ONE 10(9): e0137782. doi:10.1371/journal.pone.0137782
3. How it works?
There are two types of samples, A and B, in the matrix V, each with four replicates. Via DNMF, two matrices, Wand H, are produced. The matrix H possesses the same column number as matrix V, while the matrix Wpossesses the same row number as matrix V. To explain it biologically, the row of H could be viewed as the metagenes (the up-regulated or down-regulated gene sets), while the column of W represents the weight of each gene in the metagenes. The two metagenes in matrix H, represent the up-regulated genes and down-regulated genes respectively. Due to the correspondence between columns of W and rows of H, it is possible to identify which metagenes are combined with the up-regulated or down-regulated gene sets in matrix W. Consequently, it is easy to obtain the rank of genes by subtraction between the weights of the two metagenes in matrix W.
4. Code and bug reports