[BioC] edgeR - multiple comparisions

[Mark Robinson]

Hi Sridhara,

If you haven't already, you might have a solid read of the edgeR user's guide, it has answers to some of your questions.


On May 21, 2011, at 11:20 PM, Sridhara Gupta Kunjeti wrote:

> Hello,
> I have used edgeR for DGE analysis and I have few questions regarding the
> model and comparisions.
> 
> 1) What kind of statistical model is taken into account to analyze treatment
> structure and conduct analysis of variance?

For the example you show below (a 2-group comparison), the 'Negative binomial models' Section in the user's guide covers this.  Of course, the package has facility for more complicated "treatment structure" through generalized linear models (See the 'Experiment with multiple factors' Section, for example).


> 2) How does the edgeR correct the multiple comparisions?

See ?topTags; its also mentioned in the user's guide.

----
     topTags(object, n=10, adjust.method="BH", sort.by="p.value")
...   
adjust.method: character string stating the method used to adjust
          p-values for multiple testing, passed on to ‘p.adjust’
...
----


> 3) I am assuming that the calculated  p-values in the output after
> performing the tagwiseDispersion are after adjusting for multiple testing.
> Please correct me if I am wrong? If so, what kind of multiple testing is
> taken into account?

exactTest() doesn't do the multiple testing correction, but topTags() does.

HTH,
Mark


> 
> The arguments that I passed are as follows:
>> raw.data <- read.delim("c33_con3.txt")
>> raw.data.2a <- read.delim ("2c33_con3.txt")
>> d2a <- raw.data.2a[, 2:5]
>> rownames(d2a) <- raw.data.2a[,1]
>> group2a <- c(rep("c33", 2), rep("con3", 2))
>> d2a <- DGEList(counts = d2a, group = group2a)
>> d2a <- estimateCommonDisp(d2a)
>> d2a <- estimateTagwiseDisp(d2a, prior.n = 10, grid.length = 500)
>> prior.n2a <- estimateSmoothing(d2a)
>> de2a.tgw <- exactTest(d2a, common.disp = FALSE)
>> de2a.tgw
> An object of class "DGEExact"
> $table
> 
> logConc       logFC   p.value
> MGG_00005 | Mo hypothetical protein (1014 nt)
> -16.67772  0.05248378 0.9394668
> MGG_00015 | Mo catechol O-methyltransferase (1102 nt)
> -14.68066  0.36189877 0.2786389
> MGG_00016 | Mo 2-epi-5-epi-valiolone synthase (1739 nt)
> -13.50677  0.32379041 0.3759259
> MGG_00017 | Mo L-aminoadipate-semialdehyde dehydrogenase (3472 nt) -14.28686
> -0.35747999 0.3040601
> MGG_00018 | Mo integral membrane protein (2504 nt)
> -14.56791  0.45187243 0.1701996
> 11452 more rows ...
> $comparison
> [1] "c33"  "con3"
> $genes
> NULL
> 
> 
>> sessionInfo()
> R version 2.12.1 (2010-12-16)
> Platform: i386-pc-mingw32/i386 (32-bit)
> locale:
> [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
> States.1252    LC_MONETARY=English_United States.1252
> [4] LC_NUMERIC=C                           LC_TIME=English_United
> States.1252
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
> other attached packages:
> [1] edgeR_2.0.3
> loaded via a namespace (and not attached):
> [1] limma_3.6.9  tools_2.12.1
> 
> I would really appreciate your comments or suggestions.
> 
> Many thanks!
> 
> Sridhara
> 
> -- 
> Sridhara G Kunjeti
> PhD Candidate
> University of Delaware
> Department of Plant and Soil Science
> email- sridhara at udel.edu
> Ph: 832-566-0011
> 
> 	[[alternative HTML version deleted]]
> 
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------------------------------
Mark Robinson, PhD (Melb)
Epigenetics Laboratory, Garvan
Bioinformatics Division, WEHI
e: mrobinson at wehi.edu.au
e: m.robinson at garvan.org.au
p: +61 (0)3 9345 2628
f: +61 (0)3 9347 0852
------------------------------


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