NMFpval: P value for discriminant Non-Negative Matrix Factorization

In DNMF: Discriminant Non-Negative Matrix Factorization

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

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)

DNMF documentation built on May 10, 2022, 5:12 p.m.