Hi,

I have a related question:

After using MAS5 or RMA, I use lmFit and eBayes to comapre and get the M
value.

Since the M value scale from (rma lmFit and eBayes) are much smaller than M
value scale from (mas5 lmFit and eBayes), are their M value both in log2
scale?

Thanks a lot,


My code is as following:


DataA.4repli = ReadAffy(filenames = c("tar18.cel","tar19.cel","tar20.cel","
tar21.cel","tar22.cel","tar23.cel","tar24.cel","tar25.cel","tar26.cel","
tar27.cel","tar28.cel","31tar12.cel","31tar13.cel","31tar14.cel","
31tar15.cel","31tar16.cel","31tar17.cel","31tar18.cel","31tar19.cel"),phenoData
= matrix.A, verbose = TRUE)

esetRMA.basic.anova <- rma(DataA.4repli)
esetMAS5.basic.anova <- mas5(DataA.4repli)


exprs(esetRMA.basic.anova)
exprs(esetMAS5.basic.anova)
###

 fit.A <- lmFit(esetRMA.basic.anova,matrix.A)# t test, treat two baseline
together, the 2 exp seperate
contrast.matrix.A <- makeContrasts( glc30-con,glc37 -con, gal30 -
glc30,gal37 - glc37,glc37-glc30,gal37 - gal30, levels=matrix.A)
fit.con.A <- contrasts.fit(fit.A, contrast.matrix.A)
 fit.result.A <- eBayes(fit.con.A)
 topTable( fit.result.A , coef=3, adjust="fdr",sort.by="M", 9335)

fit.A.mas5<- lmFit(esetMAS5.basic.anova,matrix.A)
  fit.con.A.mas5 <- contrasts.fit(fit.A.mas5, contrast.matrix.A)
 fit.result.A.mas5 <- eBayes(fit.con.A.mas5)
 topTable( fit.result.A.mas5 , coef=3, adjust="fdr",sort.by="M", 9335)

Di
Biostatistician
Monash Institute of Medical Research
VIC, 3168





On 8/25/06, Rafael A. Irizarry <ririzarr@jhsph.edu> wrote:
>
> rma-log2
> mas5-original
> dchip-original (i think)
>
> as a general rule if the scale is around 0 to 16 its log2 if
> its around 0 to 40000 its original scale.
>
>
> On Thu, 24 Aug 2006, He, Yiwen (NIH/CIT) [C] wrote:
>
> > Hi,
> >
> >
> >
> > I'm trying to compare the probe level data analysis tools including
> > mas5, rma, dchip, and plier. Using the affy package and plier package, I
> > was testing on the affybatch.example data.
> >
> >
> >
> > Here's my code:
> >
> >
> >
> >> sessionInfo()
> >
> > Version 2.3.0 (2006-04-24)
> >
> > i386-pc-mingw32
> >
> >
> >
> > attached base packages:
> >
> > [1] "tools"     "methods"   "stats"     "graphics"  "grDevices" "utils"
> > "datasets"
> >
> > [8] "base"
> >
> >
> >
> > other attached packages:
> >
> >   plier     affy   affyio  Biobase
> >
> > "1.4.0" "1.10.0"  "1.0.0" "1.10.0"
> >
> >
> >
> >> data(affybatch.example)
> >
> >
> >
> > #########################
> >
> > # MAS5:
> >
> > #########################
> >
> >
> >
> >> esetMAS <- mas5(affybatch.example)
> >
> > background correction: mas
> >
> > PM/MM correction : mas
> >
> > expression values: mas
> >
> > background correcting...done.
> >
> > 150 ids to be processed
> >
> >
> >
> >> dataMAS <- exprs(esetMAS)
> >
> >
> >
> >> dataMAS[1:10,]
> >
> >                  20A       20B      10A
> >
> > A28102_at    15.46299 26.674671 12.68777
> >
> > AB000114_at  14.95012 32.449716 15.00104
> >
> > AB000115_at  14.86081 23.366739 12.84647
> >
> > AB000220_at  13.06876 16.581650 14.25944
> >
> > AB002314_at  13.25265  7.556996 13.39463
> >
> > AB002315_at  12.84931  8.891005 12.54743
> >
> > AB002318_at  15.81936 16.666234 16.58733
> >
> > AB002365_at  24.55800 22.789565 14.79018
> >
> > AB002366_at 318.06190 14.877511 19.96851
> >
> > AC000099_at  13.77299 15.014392 13.15784
> >
> >
> >
> > #########################
> >
> > # RMA:
> >
> > #########################
> >
> >
> >
> >> esetRMA <- rma(affybatch.example)
> >
> > Background correcting
> >
> > Normalizing
> >
> > Calculating Expression
> >
> >
> >
> >> dataRMA <- exprs(esetRMA)
> >
> >
> >
> >> dataRMA[1:10,]
> >
> >                 20A      20B      10A
> >
> > A28102_at   4.619839 5.429170 4.456409
> >
> > AB000114_at 4.529760 6.005186 4.673793
> >
> > AB000115_at 4.518184 4.949890 4.654417
> >
> > AB000220_at 4.422690 4.450132 4.422690
> >
> > AB002314_at 4.335935 3.950547 4.488247
> >
> > AB002315_at 4.235200 3.842533 4.312301
> >
> > AB002318_at 4.494510 4.494510 4.494510
> >
> > AB002365_at 4.679239 4.421983 4.421983
> >
> > AB002366_at 4.526551 3.919298 4.243068
> >
> > AC000099_at 4.308961 4.308961 4.221789
> >
> >
> >
> > #########################
> >
> > # dChip:
> >
> > #########################
> >
> >
> >
> >> esetDchip <- expresso(affybatch.example,
> > normalize.method="invariantset", bg.correct=F,
> > pmcorrect.method="pmonly", summary.method="liwong")
> >
> >
> >
> >> dataDchip <- exprs(esetDchip)
> >
> >
> >
> >> dataDchip[1:10,]
> >
> >                  20A       20B       10A
> >
> > A28102_at   101.81628 133.90870  99.43829
> >
> > AB000114_at 100.92215 145.67048 106.54000
> >
> > AB000115_at  94.79908 127.11273  95.28790
> >
> > AB000220_at  97.58051 103.37127 100.09415
> >
> > AB002314_at  99.53569  83.58326 104.83590
> >
> > AB002315_at  97.15775  86.10468 101.93582
> >
> > AB002318_at 101.65877  99.06988 100.42416
> >
> > AB002365_at 107.30458  99.97733  97.87809
> >
> > AB002366_at 105.38395  90.52626  98.17004
> >
> > AC000099_at 112.49714 123.53361 106.67597
> >
> >
> >
> > #########################
> >
> > # PLIER:
> >
> > #########################
> >
> >> esetPLIER <- justPlier(affybatch.example)
> >
> >
> >
> >> dim(exprs(esetPLIER))
> >
> > [1] 150   3
> >
> >
> >
> >> dataPLIER <- exprs(esetPLIER)
> >
> >
> >
> >> dataPLIER[1:10,]
> >
> >                   20A        20B        10A
> >
> > A28102_at   -11.486192 -11.479329 -11.784546
> >
> > AB000114_at -11.460412 -11.496415 -11.444121
> >
> > AB000115_at -11.772116 -11.576864 -11.598807
> >
> > AB000220_at -10.877553 -11.411278 -10.707376
> >
> > AB002314_at -11.243266 -10.853202 -10.849639
> >
> > AB002315_at -11.240642 -10.892680 -10.857467
> >
> > AB002318_at   3.352295   1.780319   1.079215
> >
> > AB002365_at   3.533914   1.561504   2.719863
> >
> > AB002366_at   3.972462  -7.970215   1.086926
> >
> > AC000099_at   4.311271   4.325806   3.339701
> >
> >
> >
> > It seems that the expression values from 4 methods are on very different
> > scales. Based on a previous message at
> > https://stat.ethz.ch/pipermail/bioconductor/2005-May/009146.html,
> > justPlier seems to have converted the data to log2. I'm wondering about
> > the other methods - mainly, are they in log space or linear space? Do
> > people typically see such big difference in the expression values, or is
> > it something specific to this sample data?
> >
> >
> >
> > Can anyone please help clarifying this?
> >
> >
> >
> > Thank you!
> >
> >
> >
> > Yiwen He
> >
> > Contractor, SRA International Inc.
> >
> > Bioinformatics and Molecular Analysis Section
> >
> > Center for Information Technology
> >
> > National Institute of Health
> >
> > --------
> >
> > Bldg. 12A, Rm. 1018
> >
> > 12 South Dr., Bethesda, MD 20892
> >
> > Email: heyiwen@mail.nih.gov
> > Phone: 301-402-4636
> >
> > Fax: 301-402-2867
> >
> >
> >
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@stat.math.ethz.ch
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >
>
> _______________________________________________
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>

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