Closed zhaoliang0302 closed 2 years ago
Hi @zhaoliang0302 , sorry to hear that. Can you describe more about your problem? What are in the inputs and parameter settings for running DeMixT? I will try to help you fix the problem. Thanks.
The input file is the TCGA tumor transcriptome raw count matrix, with over 50000 genes and 800 samples. After the DeMixT_preprocessing
process, the filtered data contains about 9000 genes. I followed the tutorial using the code:
nspikesin_list = c(0, 50, 100, 150)
ngene.selected_list = c(500, 1000, 1500, 2500)
for(nspikesin in nspikesin_list){
for(ngene.selected in ngene.selected_list){
name = paste("PRAD_demixt_GS_res_nspikesin", nspikesin, "ngene.selected",
ngene.selected, sep = "_");
name = paste(name, ".RData", sep = "");
res = DeMixT(data.Y = data.Y,
data.N1 = data.N1,
ngene.selected.for.pi = ngene.selected,
ngene.Profile.selected = ngene.selected,
filter.sd = 0.7, # same upper bound of gene expression standard deviation
# for normal reference. i.e., preprocessed_data$sd_cutoff_normal[2]
gene.selection.method = "GS",
nspikein = nspikesin)
save(res, file = name)
}
}
It cost me about 2 hours and seems stuck. I think the pairwise loop costs too much time, then I changed the code as follows:
nspikesin = 100
ngene.selected = 1500
name = paste("PRAD_demixt_GS_res_nspikesin", nspikesin, "ngene.selected",
ngene.selected, sep = "_");
name = paste(name, ".RData", sep = "");
res = DeMixT(data.Y = data.Y,
data.N1 = data.N1,
ngene.selected.for.pi = ngene.selected,
ngene.Profile.selected = ngene.selected,
filter.sd = 0.7, # same upper bound of gene expression standard deviation
# for normal reference. i.e., preprocessed_data$sd_cutoff_normal[2]
gene.selection.method = "GS",
nspikein = nspikesin)
save(res, file = name)
Finally, it works. However, the output tumor-specific expression contains only ~7000 genes. The genes interested were missing in the final data. Is it the expected result?
dim(res$ExprT)
# 7318 889
Thanks!
@zhaoliang0302 Sorry for the delayed response. DeMixT filters out the genes with low difference of expression between tumor and normal components, as well as those having large expression variation in the tumor component. So output result with fewer number of genes is expected. Since your data has a large number of samples, you can increase nspikesin to 200, and run again. Thanks.
Hi, I followed the tutorial https://wwylab.github.io/DeMixT/tutorial.html. After running
DeMixT
for about 20min, the r console shows:and then stuck. The CPU usage dropped from 100% to 30%. I don't know if the program is still running. My PC is windows 10 64bit, with 6 cores 12 threads. Thanks!