Method Of P Value Adjustment For Multivate Test In R

Question

[J.F.Jiang]

Hi All,

Here I came across a problem that I got a set of p values, e.g. 100 pvalues, but these p values are not independent

Then how can I do the pvalue adjustment for this set?

In R package, it seems that p.adjust is mainly developed for idependent hypothesises.

Thanks!

###############newly added (20th Feb 2013) Here is my situation, I just got a list of 100 p-values, some of which are associated.

For details, these 100 pvalues could be seperated into 7 groups, each group was calculated from a expression dataset. However, these datasets had 10%~60% overlap among each other, that is the pvalues inside a dataset were independent, but were not across the dataset, which bring the whole 100 p-values not independent.

So is there any solutions you know to directly and simply solve this? Some ref. pointed out to use the BH adjustment inside the R package, while choosing a loose criteria, e.g. 0.2, to define the significance level.

Answer 1

[David Quigley]

There are some very good answers in this CrossValidated thread.

[J.F.Jiang]

brilliant, thanks a lot

[J.F.Jiang]

Hi David, actually, those methods in the website you offered are mainly discussed for GWAS data. Here is my situation, I just got a list of 100 p-values, some of which are associated.

For details, these 100 pvalues could be seperated into 7 groups, each group was calculated from a expression dataset. However, these datasets had 10%~60% overlap among each other, that is the pvalues inside a dataset were independent, but were not across the dataset, which bring the whole 100 p-values not independent.

So is there any solutions you know to directly and simply solve this? Some ref. pointed out to use the BH adjustment inside the R package, while choosing a loose criteria, e.g. 0.2, to define the significance level.