[BioC] decideTests with nestedF

[James W. MacDonald]

Hi Pedro,

Pedro López Romero wrote:
> 
> Hi Jim, 
> 
> 
>>Not sure I understand your point. Are you saying that a particular 
>>contrast that appears to be significant using your method ends up having 
>>a very large p-value if you use nestedF?
> 
> 
> 
> Not exactly. The problem is that using decideTests(..., method="nestedF")
> with the whole set of genes  (not filtered by any method), some of the
> selected genes have an adjusted p-value quite large, corresponds to genes
> with a very small M value.- Theses genes are in the list of differentially
> exprssed genes  selected with decideTests(..., method="nestedF").
> 
> For example, here I show you two of the genes for a particular contrast that
> would be selected using decideTests(...,method="nestedF" )
> 
> 
> 	M	A		t		P.Val		adj.P.Val
> 1.064610409	13.0019494	4.18633292	0.000240894	0.239943977
> 0.489386597	8.228402648	2.86816475	0.007621841	0.817992583
> 
> 
> I am a bit confused with this result?, Any clue?

Your above statement is a bit confusing, but if I assume that you are 
comparing decideTests() with method="nestedF" using all genes, then with 
only those genes that have a significant F-statistic, then yes I think I 
know what is happening.

You have to understand that the adjusted p-value is a function of n, 
where n is the number of simultaneous comparisons you are doing. If you 
are filtering out a large number of genes, then the p-value adjustment 
will be less extreme because there are much fewer comparisons to adjust 
for. This is one of the main reasons people like to pre-filter their 
data - it increases their ability to detect differential expression 
because n is smaller.

Does that help?

Best,

Jim


> 
> Pedro.- 
> 
> 
> 


-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623


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