DNMF (version 1.4.2)

NMFpval: 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

nmf_res

result from DNMF or dNMF

np

number of permutations

ncores

cores used. Default is all the availiable cores

fdr

false discovery rate. Default is FALSE

top

only include top ranked genes. Default is 1000

verbose

verbose

Value

a matrix with columns rnk, p (and fdr)

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.

Examples

# NOT RUN {
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)

# }