Description Usage Arguments Details Value Author(s) Examples
Estimate the significance of differentially expressed genes in parallel.
1 2 | 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
1 2 3 4 5 6 7 8 | 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.