[BioC] The units for limma toptable logFC and AveExpr?

[Henrik Bengtsson]

Hi/FYI,

this is probably because extractExpressionSet() of the
aroma.affymetrix package returned non-logged chip-effect signals by
default.  This has now been fixed so that it returns log2 signals.

/Henrik
(author of aroma.affymetrix)

On Thu, Jul 14, 2011 at 9:15 AM, Ying Chen <Ying.Chen at imclone.com> wrote:
> Hi guys,
>
> I am new to the limma package. I read the user's guide and it says the logFC and AveExpr are log2 values.
>
> I just tried a test run and found logFC and AveExpr values are really high. For example, following are the values for probe 6733_at (affy HT_HG-U133_Plus_PM custom CDF probe):
>
>
>            ID       logFC    AveExpr         t      P.Value adj.P.Val         B
> 14512  6773_at    45.31332   71.14588  27.48457 3.475838e-04 0.5899933 -3.490399
>
> But when I look at the RMAed data before lmFit:
>
>                B02.cel c03.cel c04.cel e04.cel
> X6733_at        96.87915039     253.5151215        256.6256409        96.09108734
>
> B02 and e04 are responders, while c03 and c04 are non-responders. For toptable, coef="RESPONDERvsNON_RESPONDER".
>
> RMA was done in aroma.affymetrix because I want to use custom CDF.
>
> >From RMAed data, the ratio or fold change should be less than 3. How come after lmFit, the logFC is about 45?
>
> What did I do wrong?
>
> Thanks a lot for the help!
>
> Ying
>
>> library(aroma.affymetrix)
>> ces <- doRMA("Gastric", chipType="HT_HG-U133_Plus_PM,Binary,v14,Hs_ENTREZG", verbose=-5)
>> ces
>> eset <- extractExpressionSet(ces, verbose=-5)
>> library(limma)
>> targets <- readTargets("Gastric_Target.txt")
>> targets
>         Name                       FileName Response
> 1  GAF-087-P6 5500254086008100810456_B02.CEL      YES
> 2  GAF-023-P9 5500254086008100810456_C03.CEL       NO
> 3 GAM-016-P10 5500254086008100810456_C04.CEL       NO
> 4  GAM-022-P4 5500254086008100810456_E04.CEL      YES
>> design <- cbind(NON_RESPONDER=1,RESPONDERvsNON_RESPONDER=targets$Response=="YES")
>> design
>     NON_RESPONDER RESPONDERvsNON_RESPONDER
> [1,]             1                        1
> [2,]             1                        0
> [3,]             1                        0
> [4,]             1                        1
>> fit <- lmFit(eset,design)
>> fit <- eBayes(fit)
>> topTable(fit,coef="RESPONDERvsNON_RESPONDER")
>            ID       logFC    AveExpr         t      P.Value adj.P.Val         B
> 14481  6733_at  -158.58526  175.77775 -76.42590 2.658827e-05 0.5027575 -3.483194
> 14881  7266_at  -191.56808  498.79973 -55.97116 5.819986e-05 0.5502506 -3.484123
> 11418 54809_at   126.55865  120.14789  37.65200 1.576701e-04 0.5899933 -3.486542
> 8501   3429_at  3047.52215 1585.64249  36.09129 1.753662e-04 0.5899933 -3.486931
> 12470 56623_at   -54.64022   98.54923 -32.38532 2.302226e-04 0.5899933 -3.488091
> 8143   3156_at  -192.12436  243.72574 -32.15756 2.343398e-04 0.5899933 -3.488176
> 2949   1286_at   -73.42559   47.14983 -30.94777 2.580292e-04 0.5899933 -3.488657
> 9626   4259_at -1587.50317 1495.69604 -30.48283 2.680259e-04 0.5899933 -3.488857
> 5704  23312_at    71.01854  104.03263  28.82746 3.083598e-04 0.5899933 -3.489649
> 14512  6773_at    45.31332   71.14588  27.48457 3.475838e-04 0.5899933 -3.490399
>> write.table(topTable(fit,coef="RESPONDERvsNON_RESPONDER",n=18910),"Gastric_ENTREZG_toptable.txt",sep="\t")
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