[BioC] two color arrays normalization
Giusy Della Gatta
Gatta at icg.cpmc.columbia.edu
Wed Feb 18 19:06:09 CET 2009
Thank you Jenny!
Not only the controls but all the
arrays are going all the way round!
With yours advices the values
are (correctly) switched:
> fit$coef[1:5,]
ctrl treat
[1,] -0.024321790 0.03127735
[2,] -0.022173037 0.04670783
[3,] -0.007570963 0.05101542
[4,] -0.144515478 0.76697238
[5,] 0.012584011 0.09071233
> fit2$coef[1:5,]
ctrl treat
[1,] 0.024321790 -0.03127735
[2,] 0.022173037 -0.04670783
[3,] 0.007570963 -0.05101542
[4,] 0.144515478 -0.76697238
[5,] -0.012584011 -0.09071233
but still when I am printing out the M values
for all the genes from the MAlist object the values are not switched,
while if I try to recover the M values from the
fit2 object I don't find them.
Please, may you help me also with this?
Thank you very much
Giusy
-----Original Message-----
From: Jenny Drnevich [mailto:drnevich at illinois.edu]
Sent: Wed 2/18/2009 11:51 AM
To: Giusy Della Gatta; Naomi Altman; bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] two color arrays normalization
Hi Giusy,
It shouldn't matter if you put the minus signs in the design matrix
or the contrast matrix, they will do the same thing. Actually, the
contrast matrix is completely unnecessary, the columns of the design
matrix already specify the differences between the ref and your two
other groups. Now, are you having trouble getting the results
switched, or is it just that the results for a few genes are the
opposite of what you expect to happen? Let me walk you through a way
to check that the directions of the M values are being reversed. Your
design matrix is:
>Filename Cy3 Cy5
>Control_1b.txt ctrl ref
>Control_2b.txt ctrl ref
>Control_3b.txt ctrl ref
>dexa_dbz_lt_1.txt treat ref
>dexa_dbz_lt_2.txt treat ref
>dexa_dbz_lt_3.txt treat ref
Therefore, the M values in your MA object are log2(Cy5/Cy3), which is
either log2(ref/ctrl) or log2(ref/treat). A positive M value means up
in ref compared to the ctrl (or treat), but you really want the
opposite, that positive values mean up in ctrl (or treat) as compared
to the ref. Your design matrix as created by modelMatrix is:
ctrl treat
[1,] 1 0
[2,] 1 0
[3,] 1 0
[4,] 0 1
[5,] 0 1
[6,] 0 1
Even though the column names say "ctrl" and "treat", they actually
mean "ref-ctrl" and "ref-treat"; this is because the first column
indicates the M values from the first three arrays in the original
orientation, which is log2(Cy5/Cy3), or log2(ref) - log2(ctrl). If
you use lmFit with this design matrix:
fit<-lmFit(MA,design)
The fit$coef values will be positive or negative, "up" or "down" in
the ref as compared to the ctrl (or treat). There are many different
ways to flip these, your contrast matrix with -1s is one way, but a
quicker way is to just multiply the original design matrix by -1:
fit2 <- lmFit(MA, design*-1)
Now, compare the direction of change between the two fit objects:
fit$coef[1:5,]
fit2$coef[1:5,]
The magnitude of the values shouldn't change, but the direction
should be switched. If you are absolutely sure that the ref was in
Cy5 on your arrays, then the fit2 object should contain the correct
orientation of "up" or "down" in the ctrl as compared to the ref.
However, if the positive controls are going in the opposite direction
of the way you expect them to be, it's not because you are setting up
the contrasts incorrectly. Either you have somehow switched the
samples on the arrays, or the probes for the positive control are
measuring a different part of the transcript that give a different
result than you expect.
HTH,
Jenny
At 09:13 AM 2/18/2009, Giusy Della Gatta wrote:
>Hi Naomi,
>I don't know if I understood correct. I switched the signs of the
>design and the
>contrast matrices, but I still have the same results: controls going
>at the opposite way.
>
> > library(limma)
> > # Read in data files
> > targets=readTargets("target_frame_ltbarc_pgedit.giusy")
> > RG<-read.maimages(targets$FileName, source="agilent", ext="txt")
>Read Control _1b.txt
>Re
>Read Control_3b.txt
>Read dexa_dbz_lt_1.txt
>Read dexa_dbz_lt_2.txt
>Read dexa_dbz_lt_3.txt
> > # create MA list
> > MA<-MA.RG(RG, bc.method="none")
> > # perform background correction
> > RG<-backgroundCorrect (RG,method="none")
> > # perform within array normalization
> > MA<-normalizeWithinArrays(RG, method="loess")
> > # Create design matrix
> > design <- modelMatrix(targets, ref="ref")
>Found unique target names:
> ctrl ref treat
> > design<- cbind(ctrl= c(1,1,1,0,0,0), treat= c(0,0,0,1,1,1))
> > design
> ctrl treat
>[1,] 1 0
>[2,] 1 0
>[3,] 1 0
>[4,] 0 1
>[5,] 0 1
>[6,] 0 1
> > fit<-lmFit(MA,design)
> > cont.matrix<-cbind("ctrl-ref"=c(-1,0), "treat-ref"=c(0,-1))
> > cont.matrix
> ctrl-ref treat-ref
>[1,] -1 0
>[2,] 0 -1
> > fit2<-contrasts.fit(fit, cont.matrix)
> > d1 <- ebayes(fit2)
>
>
>
>
>Thank you
>Giusy
>
>
>-----Original Message-----
>From: Naomi Altman [mailto:naomi at stat.psu.edu]
>Sent: Tue 2/17/2009 7:50 PM
>To: Giusy Della Gatta; Naomi Altman; bioconductor at stat.math.ethz.ch
>Subject: RE: [BioC] two color arrays normalization
>
>Hi Giusy,
>Move the minus signs from the first design matrix to the 2nd and I
>think it will work fine.
>
>--Naomi
>
>At 06:07 PM 2/17/2009, Giusy Della Gatta wrote:
> >Hi Naomi,
> >
> >I performed the analysis of my micorarrays,but still I don't manage
> >to revert the channels!
> >My experiment consisted into infect cells with an adenovirus: an emty one
> >and an adenovirus expressing for a specific protein. Then I
> >treated the same cells with a specific drug or simply with the
> vehicle (DMSO).
> >I have 6 microarrays: 3 controls DMSO0-treated and 3 samples drug-treated.
> >In each microarray the green channel is expressing the levels of
> >infected and treated
> >cells while the red channel are not infected cells. For all the experiments
> >I have the same RED CHANNEL reference.
> >I composed the target file as follows:
> >
> >Filename Cy3 Cy5
> >Control_1b.txt ctrl ref
> >Control_2b.txt ctrl ref
> >Control_3b.txt ctrl ref
> >dexa_dbz_lt_1.txt treat ref
> >dexa_dbz_lt_2.txt treat ref
> >dexa_dbz_lt_3.txt treat ref
> >
> >and the I used the following script:
> >
> > >targets=readTargets("target_frame_ltbarc_pgedit.giusy")
> > >RG<-read.maimages(targets$FileName, source="agilent", ext="txt")
> > >MA<-MA.RG(RG, bc.method="normexp")
> > >MA<-normalizeWithinArrays(RG, method="loess")
> > >design <- modelMatrix(targets, ref="ref")
> > > design
> > ctrl treat
> >[1,] -1 0
> >[2,] -1 0
> >[3,] -1 0
> >[4,] 0 -1
> >[5,] 0 -1
> >[6,] 0 -1
> > >fit<-lmFit(MA,design)
> > >cont.matrix<-cbind("ctrl-ref"=c(1,0), "treat-ref"=c(0,1))
> > >cont.matrix
> > ctrl-ref treat-ref
> >[1,] 1 0
> >[2,] 0 1
> >
> > >fit2<-contrasts.fit(fit, cont.matrix)
> > >d1 <- ebayes(fit2)
> > >toptable(fit2,adjust="fdr")
> >
> >I don't know if I am still omitting
> >something, because I have the positive
> >controls of this experiment that
> >are going exactly in the opposite way!!
> >
> >May you can help me?
> >
> >Thank you in advance!
> >Giusy
> >
> >
> >-----Original Message-----
> >From: Naomi Altman [mailto:naomi at stat.psu.edu]
> >Sent: Mon 2/9/2009 9:56 PM
> >To: Giusy Della Gatta; bioconductor at stat.math.ethz.ch
> >Subject: Re: [BioC] two color arrays normalization
> >
> >If there is no dye-swap, then what do you mean by "swapping of the colors"?
> >
> >--Naomi
> >
> >At 07:56 PM 2/9/2009, Giusy Della Gatta wrote:
> >
> > >Hi everybody,
> > >
> > >I am analyzing two color Agilent microarrays
> > >by using LIMMA package.
> > >In my specific case the red channel is representing
> > >"the reference" while the green channel is "the treatment".
> > >Is it enough to use the Target File composition to specify the name
> > >of the samples
> > >and their corrispondet channels? Or I have to use other specific commands
> > >to specify the "swapping" of the colors?
> > >
> > >Thank you in advance!
> > >Regards
> > >Giusy
> > >
> > >_______________________________________________
> > >Bioconductor mailing list
> > >Bioconductor at stat.math.ethz.ch
> > >https://stat.ethz.ch/mailman/listinfo/bioconductor
> > >Search the archives:
> > >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
>
>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
>https://stat.ethz.ch/mailman/listinfo/bioconductor
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Jenny Drnevich, Ph.D.
Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign
330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA
ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich at illinois.edu
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