P value for discriminant Non-Negative Matrix Factorization

Description

Estimate the significance of differentially expressed genes in parallel.

Usage

NMFpval(nmf_res, np = 100, ncores = parallel::detectCores(), fdr = FALSE,
  top = 1000, verbose = FALSE)

Arguments

Details

P value is caculated based on aatricle, Wang, Hong-Qiang, Chun-Hou Zheng, and Xing-Ming Zhao. "jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data." Bioinformatics (2014): btu679.

Value

a matrix with columns rnk, p (and fdr)

Author(s)

Zhilong Jia

Examples

dat <- rbind(matrix(c(rep(3, 16), rep(8, 24)), ncol=5),
matrix(c(rep(5, 16), rep(5, 24)), ncol=5),
matrix(c(rep(18, 16), rep(7, 24)), ncol=5)) +
matrix(runif(120,-1,1), ncol=5)
trainlabel <- c(1,1,2,2,2)

nmf_res <- ndNMF(dat, trainlabel, r=2, lambada = 0.1)
pMat <- NMFpval(nmf_res, np=10, ncores=2, top=4)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(DNMF)
Loading required package: foreach
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DNMF/NMFpval.Rd_%03d_medium.png", width=480, height=480)
> ### Name: NMFpval
> ### Title: P value for discriminant Non-Negative Matrix Factorization
> ### Aliases: NMFpval
> 
> ### ** Examples
> 
> dat <- rbind(matrix(c(rep(3, 16), rep(8, 24)), ncol=5),
+ matrix(c(rep(5, 16), rep(5, 24)), ncol=5),
+ matrix(c(rep(18, 16), rep(7, 24)), ncol=5)) +
+ matrix(runif(120,-1,1), ncol=5)
> trainlabel <- c(1,1,2,2,2)
> 
> nmf_res <- ndNMF(dat, trainlabel, r=2, lambada = 0.1)
> pMat <- NMFpval(nmf_res, np=10, ncores=2, top=4)
Time difference of 1.679775 secs
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>