NMFpval | R Documentation |
Estimate the significance of differentially expressed genes in parallel.
NMFpval( nmf_res, np = 100, ncores = parallel::detectCores(), fdr = FALSE, top = 1000, verbose = FALSE )
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 |
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.
a matrix with columns rnk, p (and fdr)
Zhilong Jia
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)
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