How to use "p-value" instead of "p-adj value" in DESeq2
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@4fedfa78
Last seen 8 days ago
Japan

Hello everyone,

I would like to use only "p- value" instead of "p-adj value" in DESeq2. but I could not find the proper command for doing so. While I used the below command but I think it is not correct and gives me error. is there any one to assist me in this case? Thank you

table(res$pvalue)
summary(results(dds, pvalue= 0.0037, lfcThreshold = 0.5))

Error: unexpected '=' in "summary(results(dds, res$pvalue="

# include your problematic code here with any corresponding output 
# please also include the results of running the following in an R session 

sessionInfo( )
DESeq2 • 432 views
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@mikelove
Last seen 1 hour ago
United States

results() does not filter. You can do that afterward using any R code for manipulating a data.frame.

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Thank you for your response, sorry but I not got exactly your point, I am looking for command to have just raw p-value, because this command is rejected.

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You can use standard R code to filter or work with a results table. See our workflow for some example code before going further.

res <- results(...)

res is a type of data frame (it's a Bioconductor DataFrame), so you can use whatever steps you like to work with it. You can even use "tidyverse" commands if you prefer, by converting it to a regular data.frame first:

res <- as.data.frame(res)

Then you could do e.g.:

library(dplyr)
res_filt <- res %>% filter(padj < .01)

Or in base R:

res_filt <- subset(res, padj < .01)
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many thanks for your reply, I tried this command but again it gave me the counts based on padj-value not raw p-value, what is the solution? is it there some commands to do so?

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We recommend to use adjusted p-value and not p-value. See the section of the workflow that describes why:

https://www.bioconductor.org/packages/devel/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html

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I agree with you, but when I am using adjusted p-value, and trying different adjusted p-values, but it gives me nearly zero up and down regulation counts, In this case what is your suggestion to me?

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That means you cannot reliably call DE genes in this dataset.

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Thanks again for your reply. I followed different tutorial of DESeq2, there is not any problem, just the final results is wrong. could you please advice me what to do in such situation?

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Final result is not wrong, it’s just saying something different than what you were expecting.

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many thanks, so what is your suggestion to me in this case?

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