Hello,
I was looking for pearson correlation between a gene expression and the drug sensitivity dataset using the custom analyses tool. I am wondering how the q values are calculated because they are very small compared to the adjusted p-values I calculated myself using BH methods.
Thank you so much for your help!
This thread provides links to the methodology / underlying methods - that might explain the difference you’re seeing:
Thank you for your response!
In the R script, the function p.adjust(method = ‘BH’) was used and I used the same function but I still can’t get the same p-value.
And the link you suggested me is referring to two-class comparison tool, but I would like to know about the pearson correlation. Can I have the link to that script also?
1 Like
For the pearson correlations, we are using using R’s p.adjust(method=‘fdr’). According to the docs, that ‘fdr’ should be synonymous with ‘BH’, it should report the same q-value.
However, looking at the UI, I don’t see a way to download the q-values, only the uncorrected p-values. (I imagine that was an oversight in making the download. Our plot has the uncorrected p-value and we’re coloring by a threshold on the q-value, so I imagine it’d be good to include both in the download.)
Are you looking at the p-value column and expecting it to be a corrected p-value? If so, that’s likely the source of confusion.
Thanks,
Phil
1 Like
Hello,
I know the plot only shows uncorrected p-values, so I downloaded the whole table to get the q-values. However, when I use those uncorrected p-values in the downloaded table to calculated the q-value myself, the results are different compared to the q-values provided in the downloaded table. The function I used is p.adjust(df$PValue, method = ‘fdr’), in which ‘df’ is the table I downloaded from DepMap.
Thank you for your help!
Anh Nguyen
