Hi Chuming,

I have over looked your previous mail. It seems, there is nothing wrong. So, better to follow Wolfgang and Naomi suggestions.

Regards,
Prashantha

Prashantha Hebbar Kiradi,

Dept. of Biotechnology,

Manipal Life Sciences Center,

Manipal University,

Manipal, India

Email:prashantha.hebbar@manipal.edu



--- On Thu, 1/28/10, Chuming Chen <chumingchen@gmail.com> wrote:

From: Chuming Chen <chumingchen@gmail.com>
Subject: Re: [BioC] Agilent G4112A Arrays
To: "Prashantha Hebbar" <prashantha.hebbar@yahoo.com>
Cc: bioconductor@stat.math.ethz.ch
Date: Thursday, January 28, 2010, 4:50 AM

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@manipal.edu
> 
> --- On *Mon, 1/25/10, Wolfgang Huber /<whuber@embl.de>/* wrote:
> 
> 
>     From: Wolfgang Huber <whuber@embl.de>
>     Subject: Re: [BioC] Agilent G4112A Arrays
>     To: "Naomi Altman" <naomi@stat.psu.edu>
>     Cc: "Chuming Chen" <chumingchen@gmail.com>, "Prashantha Hebbar"
>     <prashantha.hebbar@yahoo.com>, bioconductor@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@gmail.com
>     <http://us.mc1101.mail.yahoo.com/mc/compose?to=chumingchen@gmail.com>>/*
>     wrote:
>     >>>
>     >>>
>     >>>     From: Chuming Chen <chumingchen@gmail.com
>     <http://us.mc1101.mail.yahoo.com/mc/compose?to=chumingchen@gmail.com>>
>     >>>     Subject: [BioC] Agilent G4112A Arrays
> 
    >>>     To: bioconductor@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@stat.math.ethz.ch
>     <http://us.mc1101.mail.yahoo.com/mc/compose?to=Bioconductor@stat.math.ethz.ch>
>     >>> 
    </mc/compose?to=Bioconductor@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@stat.math.ethz.ch
>     <http://us.mc1101.mail.yahoo.com/mc/compose?to=Bioconductor@stat.math.ethz.ch>
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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@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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