[BioC] Understanding the columns in the limma results output

[James W. MacDonald]

Hi Jorge,

On 8/28/2012 11:20 AM, Jorge Miró wrote:
> Hi,
>
> I have run the commands below to get an analysis of differential
> expressions in my Affymetrix arrays
>
> #Prepare the design and contrast matrices for my comparisons of the three
> groups in a loop-manner.
>> design<- model.matrix(~0+factor(c(1,1,1,2,2,2,3,3,3)))
>> colnames(design)<- c('GroupA', 'GroupB', 'GroupC')
>> contrast.matrix<- makeContrasts(GroupB-GroupA, GroupC-GroupA,
> GroupC-GroupB, levels=design)
>
> #Check design and contrast matrices
>> design
>    GroupA GroupB GroupC
> 1      1      0      0
> 2      1      0      0
> 3      1      0      0
> 4      0      1      0
> 5      0      1      0
> 6      0      1      0
> 7      0      0      1
> 8      0      0      1
> 9      0      0      1
> attr(,"assign")
> [1] 1 1 1
> attr(,"contrasts")
> attr(,"contrasts")$`factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3))`
> [1] "contr.treatment"
>
>> contrast.matrix
>          Contrasts
> Levels   GroupB - GroupA GroupC - GroupA GroupC - GroupB
>    GroupA              -1              -1               0
>    GroupB               1               0              -1
>    GroupC               0               1               1
>
> #Fitting the eset to to the design and contrast
>> fit<- lmFit(exprs, design)
>> fit2<- contrasts.fit(fit, contrast.matrix)
> #Computing the statistics
>> fit2<- eBayes(fit2)
>
> Then I check the results with topTable and get the following columns in the
> output
>> topTable(fit2)
>        GroupB...GroupA GroupC...GroupA GroupC...GroupB  AveExpr        F
>   P.Value adj.P.Val
> 25031       2.3602203       2.4273830      0.06716267 5.021412 29.06509
> 7.844834e-05 0.9587773
> 12902      -0.4572897      -0.5680943     -0.11080467 7.516681 25.41608
> 1.365021e-04 0.9587773
> 7158       -0.4478660      -0.4296077      0.01825833 7.057833 23.48871
> 1.880100e-04 0.9587773
> 18358      -0.1002647       0.3304903      0.43075500 7.352807 22.78417
> 2.125096e-04 0.9587773
> 28768      -0.7695883      -1.3837750     -0.61418667 3.983044 22.47514
> 2.244612e-04 0.9587773
> 28820      -0.1708800      -0.9939680     -0.82308800 5.470826 18.25071
> 5.081473e-04 0.9587773
> 15238      -0.4850297      -0.4658157      0.01921400 7.071662 17.15191
> 6.440979e-04 0.9587773
> 24681      -0.3759717      -0.3486450      0.02732667 9.281578 16.47813
> 7.493077e-04 0.9587773
> 19246      -0.8675393      -0.5082140      0.35932533 8.123538 16.27776
> 7.845150e-04 0.9587773
> 28808       0.2601277       0.6909140      0.43078633 4.814602 16.21283
> 7.963487e-04 0.9587773
>
> I want to export my results and write
>
>> results<- decideTests(fit2)
>> write.fit(fit2, results, "limma_results.txt", adjust="BH")
> Now don't get the same columns as when using topTable which is quite
> confusing. Why don't I get the FC for the comparisons between the different
> groups as if I run topTable with the coef parameter ( "topTable(fit2,
> coef=1)" )? The columns I get are the following

The simple answer is that they are two different functions with 
different goals. But note that you do get the same information.

>
> A
>
> Coef.GroupB - GroupA
> Coef.GroupC - GroupA
> Coef.GroupC - GroupB
>
> t.GroupB - GroupA
> t.GroupC - GroupA
> t.GroupC - GroupB
>
> p.value.GroupB - GroupA
> p.value.GroupC - GroupA
> p.value.GroupC - GroupB
>
> p.value.adj.GroupB - GroupA
> p.value.adj.GroupC - GroupA
> p.value.adj.GroupC - GroupB
>
> F
> F.p.value
>
> Res.GroupB - GroupA
> Res.GroupC - GroupA
> Res.GroupC - GroupB
>
>
> Could some body please try to explain what do the columns A, Coef, F,
> F.p.value and Res mean?

A - are your log fold change values
Coef - are your coefficients (you set up a cell means model, so these 
are the sample means)
F - is an F-statistic, which tests the null hypothesis that none of the 
sample means are different
F.p.value - is the p-value for the F-statistic
Res - is the results matrix you passed into write.fit(), showing which 
contrast(s) were significant

Best,

Jim


>
>
>
> #Session info
>> sessionInfo()
> R version 2.15.0 (2012-03-30)
> Platform: i386-pc-mingw32/i386 (32-bit)
>
> locale:
> [1] LC_COLLATE=Swedish_Sweden.1252  LC_CTYPE=Swedish_Sweden.1252
>   LC_MONETARY=Swedish_Sweden.1252 LC_NUMERIC=C
>   LC_TIME=Swedish_Sweden.1252
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] limma_3.12.1       Biobase_2.16.0     BiocGenerics_0.2.0
>
> loaded via a namespace (and not attached):
> [1] affylmGUI_1.30.0      IRanges_1.14.4        oneChannelGUI_1.22.10
> stats4_2.15.0         tcltk_2.15.0
> Best regards
> JMA
>
> 	[[alternative HTML version deleted]]
>
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-- 
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099