How to calculate q-values in this case

Question

[xsh6528]

I have data for gene mutations (binary values) and gene expression level (continuous values) for several patients. (These are shown in two tables in the first row of the pic.)

To decide whether a gene mutation truly affects function. I need to combine it with its expression level.

So I divided people into two groups based on gene mutation. Then do a test on their gene expression levels. This test result in a P-value.

My question is, what is the right way to convert P-values to q-values in this case,

Should I pool all P-values across groups or within a group together to calculate individual q-values?

It is actually the method used in 'Emerging landscape of oncogenic signatures across human cancers', which I intend to imitate.

http://www.nature.com/ng/journal/v45/n10/full/ng.2762.html

see Method-Testing for concordant mRNA and copy number changes.

[h.mon]

Is this homework?

[xsh6528]

It is the method used in Emerging landscape of oncogenic signatures across human cancers.

http://www.nature.com/ng/journal/v45/n10/full/ng.2762.html

see Method-Testing for concordant mRNA and copy number changes.

I want to figure out their way of preprocessing data, so that I can imitate in my research.

[apelin20]

p.adjust(p, method = p.adjust.methods, n = length(p))

[Devon Ryan]

p.adjust doesn't produce Q values, which are related to adjusted p-values but not actually the same. There happens to be a separate qvalue Bioconductor package for this purpose though (it's even written by John Storey, who came up with q-values).

[apelin20]

But let's say this wasn't a homework question and one was interested in correcting for multiple tests, would p.adjust work? It seems to implement a variety of methods

p.adjust.methods
# c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY",
#   "fdr", "none")

[Devon Ryan]

Sure, p.adjust() (I use the BH method myself) is the standard function unless one really wants/needs q-values.