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

[john herbert]

OK, I solved it using raw values and see 6.09 is log2 FC.
Thanks.

John.

On Fri, Jul 6, 2012 at 9:00 PM, john herbert <arraystruggles at gmail.com> wrote:
> 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
>>