[BioC] Limma topTable; fold changes look completely different to the normalized data and Limma fold change

[john herbert]

Thanks a lot James,
Yes, the raw PAI values are very big and I am feeding Limma log2 and
normalized values.

So if I have a log2 value of 22.59173 for Tumour and a log2 value for
wound of 16.50009333
subtracting tumour - wound = 6.09 (the same number toptable comes up with)

What is this 6.09 value, is that fold change or log2 fold change?

I would guess fold change as 2 to the power of 6 = 64 fold change but
topTable labels it as logFC; please explain why?

Thank you,

John.





>> Tumour average = 22.59173
>> Wound average = 16.50009333
>> Log2 Fold change = 0.453320567



On Fri, Jul 6, 2012 at 8:36 PM, James W. MacDonald <jmacdon at uw.edu> wrote:
> Hi John,
>
>
> On 7/6/2012 3:12 PM, john herbert wrote:
>>
>> Dear all,
>> I have a problem with the log Fold changes calculated in Limma. I am
>> using protein abundance index of proteomic data
>> The log2 of this data is normally distributed and after log2, I use
>> quantile normalization
>>
>> This is then the data matrix I use as input to Limma
>>
>>> class(norm_ctw)
>>
>> [1] "matrix"
>>
>>> dim(norm_ctw)
>>
>> [1] 683   9
>> design<- model.matrix(~ 0+factor(c(1,1,1,2,2,2,3,3,3)))
>> colnames(design)<- c("cam", "tumour", "wound")
>> fit<- lmFit(norm_ctw, design)
>>
>> contrast.matrix<- makeContrasts(tumour-wound, tumour-cam, levels=design)
>> fit2<- contrasts.fit(fit, contrast.matrix)
>> fit2<- eBayes(fit2)
>>
>> topTable(fit2, coef=1, adjust="BH")
>>
>> Taking one gene as an example. NAMPT in tumour versus wound and
>> calculating fold change by hand of normalized data;
>>
>>> norm_ctw["NAMPT",]
>>
>>      cam1     cam2     cam3  tumour1  tumour2  tumour3   wound1
>> wound2   wound3
>> 19.80164 19.46355 19.26075 22.75347 22.62651 22.39521 16.17398 16.60262
>> 16.72368
>>
>> In Excel, calculating log2 fold change using Average of Tumour/Average
>> of wound =
>> T1 22.75347     T2 22.62651     T3 22.39521     W1 16.17398     W2
>> 16.60262     W3 16.72368
>> Tumour average = 22.59173
>> Wound average = 16.50009333
>> Log2 Fold change = 0.453320567
>
>
> Wait a minute... Are these data logged or not? You say above that you take
> logs and then normalize, and then you present some data that would be really
> big if they were log2 variates (but then I have no idea of the scale for
> protein abundance data).
>
> Anyway, you are acting like these data are not logged, whereas limma assumes
> they are. So you either have to take logs before feeding into limma, or you
> need to compute the fold change by subtraction (if the data above are
> already logged).
>
> Best,
>
> Jim
>
>
>
>>
>>
>> However, from TopTable....
>>>
>>> topTable(fit2,coef=1)
>>
>>            ID     logFC  AveExpr         t      P.Value    adj.P.Val
>> B
>> 431    NAMPT  6.091632 19.53349  20.16810 2.688444e-09 1.750946e-06
>> 11.409857
>>
>> > From toptable, NAMPT has an apparent log2 FC of 6 or 64 fold change
>> but that is impossible right??
>>
>> Please can someone explain if I am using Limma wrong or how the fold
>> change can be massively different between "by hand" and with Limma.
>>
>> Thank you very much for any advice.
>>
>> John.
>>
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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
>