[BioC] Agilent G4112A Arrays
Chuming Chen
chumingchen at gmail.com
Thu Jan 28 05:50:48 CET 2010
Prashantha and all,
Here is the sessional information regarding my analysis of this data set.
Can you point out what I might do wrong?
Thanks,
Chuming
> library(limma)
>
> targets <- readTargets("Targets.txt")
> targets
SlideNumber Name FileName Cy3 Cy5
1 1 B1vsT1 US23502303_251239134396_S02_44k.txt B1 T1
2 2 B2vsT2 US23502303_251239134397_S01_44k.txt B2 T2
3 3 B3vsT3 US23502303_251239134398_S01_44k.txt B3 T3
4 4 B4vsT4 US23502303_251239134399_S01_44k.txt B4 T4
5 5 B5vsT5 US23502303_251239134400_S01_44k.txt B5 T5
>
> RG <- read.maimages(targets, source="agilent")
Read US23502303_251239134396_S02_44k.txt
Read US23502303_251239134397_S01_44k.txt
Read US23502303_251239134398_S01_44k.txt
Read US23502303_251239134399_S01_44k.txt
Read US23502303_251239134400_S01_44k.txt
>
> RG <- backgroundCorrect(RG, method="normexp", offset=50)
Green channel
Corrected array 1
Corrected array 2
Corrected array 3
Corrected array 4
Corrected array 5
Red channel
Corrected array 1
Corrected array 2
Corrected array 3
Corrected array 4
Corrected array 5
>
> plotDensities(RG)
>
> MA <- normalizeBetweenArrays(RG,method="vsn")
Loading required package: vsn
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material. To view, type
'openVignette()'. To cite Bioconductor, see
'citation("Biobase")' and for packages 'citation(pkgname)'.
vsn2: 43931 x 10 matrix (1 stratum). Please use 'meanSdPlot' to verify
the fit.
>
> plotDensities(MA)
>
> f<-factor(targets$Name)
> design<-model.matrix(~0+f)
> design
fB1vsT1 fB2vsT2 fB3vsT3 fB4vsT4 fB5vsT5
1 1 0 0 0 0
2 0 1 0 0 0
3 0 0 1 0 0
4 0 0 0 1 0
5 0 0 0 0 1
attr(,"assign")
[1] 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$f
[1] "contr.treatment"
> colnames(design) <- levels(f)
> design
B1vsT1 B2vsT2 B3vsT3 B4vsT4 B5vsT5
1 1 0 0 0 0
2 0 1 0 0 0
3 0 0 1 0 0
4 0 0 0 1 0
5 0 0 0 0 1
attr(,"assign")
[1] 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$f
[1] "contr.treatment"
>
> fit<-lmFit(MA, design)
> contrasts.matrix <- makeContrasts(B1vsT1,B2vsT2, B3vsT3, B4vsT4,
B5vsT5, levels=design)
>
> fit2 <- contrasts.fit(fit, contrasts.matrix)
> fit2 <- eBayes(fit2)
Error in ebayes(fit = fit, proportion = proportion, stdev.coef.lim =
stdev.coef.lim) :
No residual degrees of freedom in linear model fits
>
> toptable(fit2)
Error in ebayes(fit, ...) :
No residual degrees of freedom in linear model fits
>
Prashantha Hebbar wrote:
> Hi Chuming,
>
> As per your experimental information, you have replicates. Because,
> you have samples from same tissue with 2 different region across
> all patients. So, you should be able to fit linear model. What I
> guess, there is something wrong in your analysis steps. So, better to
> send sessional information to list.
>
> Regards,
> Prashantha
>
> Prashantha Hebbar Kiradi,
> Dept. of Biotechnology,
> Manipal Life Sciences Center,
> Manipal University,
> Manipal, India
> Email:prashantha.hebbar at manipal.edu
>
> --- On *Mon, 1/25/10, Wolfgang Huber /<whuber at embl.de>/* wrote:
>
>
> From: Wolfgang Huber <whuber at embl.de>
> Subject: Re: [BioC] Agilent G4112A Arrays
> To: "Naomi Altman" <naomi at stat.psu.edu>
> Cc: "Chuming Chen" <chumingchen at gmail.com>, "Prashantha Hebbar"
> <prashantha.hebbar at yahoo.com>, bioconductor at stat.math.ethz.ch
> Date: Monday, January 25, 2010, 8:06 PM
>
> Hi Chuming
>
> if you want to work with the approximation that M-values have
> equal variances, then preprocessing the data with a method that
> provides variance stabilisation (e.g. vsn) will likely be useful.
>
> Furthermore, it might be useful to discard a fraction of genes
> with low A-values, since they are more likely to be either not
> expressed, or so weakly expressed that you would find it more
> difficult to validate them.
>
> Best wishes
> Wolfgang
>
> Naomi Altman wrote:
> > The more data one has, the fewer assumptions one needs. In the
> absence of replication, you cannot get p-values without very
> strong assumptions. e.g. you could assume that the vast majority
> of the genes do not differentially express, that their M-values
> have equal variance and that the M-values are normally
> distributed. Then you could use e.g. the IQR of the M-values to
> estimate the sd and use this to pick a fold cut-off for DE. You
> have no reasonable way to estimate FDR with this approach, but it
> might be slightly better than using 2-fold - or then again, it
> might not. Without replication, there is no way to know.
> >
> > Regards,
> > Naomi Altman
> >
> >
> > At 08:53 AM 1/25/2010, Chuming Chen wrote:
> >> Hi Prashantha,
> >>
> >> Thank you for your suggestion. My target file is as below.
> Although I couldn't fit a linear model, I still wonder whether I
> can do some statistic on M (log ratio) values and use the p-value
> to get the differentially expressed genes.
> >>
> >> SlideNumber FileName Cy3 Cy5
> >> 1 B1vsT1.txt B1 T1
> >> 2 B2vsT2.txt B2 T2
> >> 3 B3vsT3.txt B3 T3
> >> 4 B4vsT4.txt B4 T4
> >> 5 B5vsT5.txt B5 T5
> >>
> >> Chuming
> >>
> >>
> >> Prashantha Hebbar wrote:
> >>> Dear Chen,
> >>>
> >>> You need not to look for any other packages. Since, you do not
> have any replicates, do not fit linear model, instead just do
> normalization with in arrays and look at the M (log ratio) values.
> >>>
> >>> Regards,
> >>>
> >>> Prashantha Hebbar Kiradi,
> >>> Dept. of Biotechnology,
> >>> Manipal Life Sciences Center,
> >>> Manipal University,
> >>> Manipal, India
> >>>
> >>>
> >>> --- On *Mon, 1/25/10, Chuming Chen /<chumingchen at gmail.com
> <http://us.mc1101.mail.yahoo.com/mc/compose?to=chumingchen@gmail.com>>/*
> wrote:
> >>>
> >>>
> >>> From: Chuming Chen <chumingchen at gmail.com
> <http://us.mc1101.mail.yahoo.com/mc/compose?to=chumingchen@gmail.com>>
> >>> Subject: [BioC] Agilent G4112A Arrays
> >>> To: bioconductor at stat.math.ethz.ch
> <http://us.mc1101.mail.yahoo.com/mc/compose?to=bioconductor@stat.math.ethz.ch>
> >>> Date: Monday, January 25, 2010, 6:32 AM
> >>>
> >>> Dear All,
> >>>
> >>> I am trying to find out the differentially expressed genes
> from
> >>> some Agilent Human Whole Genome (G4112A) Arrays data.
> >>>
> >>> I have tried LIMMA package, but LIMMA gave the error
> message "no
> >>> residual degrees of freedom in linear model fits" and
> stopped. My
> >>> guess is that my data has no replicates in the experiment.
> >>>
> >>> Is there any other packages I can use to find differentially
> >>> expressed genes which does not require replicates in the
> experiment?
> >>>
> >>> Thanks for your help.
> >>>
> >>> Chuming
> >>>
> >>> _______________________________________________
> >>> Bioconductor mailing list
> >>> Bioconductor at stat.math.ethz.ch
> <http://us.mc1101.mail.yahoo.com/mc/compose?to=Bioconductor@stat.math.ethz.ch>
> >>> </mc/compose?to=Bioconductor at stat.math.ethz.ch
> <http://us.mc1101.mail.yahoo.com/mc/compose?to=Bioconductor@stat.math.ethz.ch>>
> >>> https://stat.ethz.ch/mailman/listinfo/bioconductor
> >>> Search the archives:
> >>>
> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >>>
> >>
> >> _______________________________________________
> >> Bioconductor mailing list
> >> Bioconductor at stat.math.ethz.ch
> <http://us.mc1101.mail.yahoo.com/mc/compose?to=Bioconductor@stat.math.ethz.ch>
> >> https://stat.ethz.ch/mailman/listinfo/bioconductor
> >> Search the archives:
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> <http://news..gmane.org/gmane.science.biology.informatics.conductor>
> >
> > Naomi S. Altman 814-865-3791 (voice)
> > Associate Professor
> > Dept. of Statistics 814-863-7114 (fax)
> > Penn State University 814-865-1348
> (Statistics)
> > University Park, PA 16802-2111
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor at stat.math.ethz.ch
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> > Search the archives:
> http://news.gmane.org/gmane.science.biology..informatics.conductor
> <http://news.gmane.org/gmane.science.biology.informatics.conductor>
>
> --
> Best wishes
> Wolfgang
>
>
> --
> Wolfgang Huber
> EMBL
> http://www.embl.de/research/units/genome_biology/huber/contact
>
>
>
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