[Bioc-sig-seq] Normalization paired samples

[Mayte Suarez-Farinas]

Dear Wolfgang

I am attaching the plots with highlighted DEG tags before and after 3  
different normalization procedures, using all samples. In the second  
figure., I include the plots for the 3 patients separately before and  
after TMM normalization
It looks like the normalization for patient 2 is bad. any idea how I  
can solve this?


pdf('Normalization Plots.pdf',height=10,width=10)
layout(matrix(1:4,2,2,byrow=TRUE))
a<-plotSmear(PScounts,  
panel.first=grid(),smooth.scatter=FALSE,main='before  
normalization',de.tags=deg.tgw)
ma.plot(a$A,a$M,plot.method='add',cex=0)
b<-plotSmear(d.PS, panel.first=grid(),smooth.scatter=FALSE,main='after  
TMM',de.tags=deg.tgw)
ma.plot(b$A,b$M,plot.method='add',cex=0)
rm(b)
b<-plotSmear(d.PS.2,  
panel.first=grid(),smooth.scatter=FALSE,main='after  
RLE',de.tags=deg.tgw)
ma.plot(b$A,b$M,plot.method='add',cex=0)
rm(b)
b<-plotSmear(d.PS.3,  
panel.first=grid(),smooth.scatter=FALSE,main='after  
quantile',de.tags=deg.tgw)
ma.plot(b$A,b$M,plot.method='add',cex=0)
rm(b)
dev.off()

pdf('Normalization Plots TMM by pairs.pdf',height=10,width=14)
layout(matrix(1:6,2,3,byrow=TRUE))
a<-plotSmear(PScounts[,c(1,4)],  
panel.first=grid(),smooth.scatter=FALSE,main='Patient1 before',  
de.tags=deg.tgw, ylim=c(-10,10))
ma.plot(a$A,a$M,plot.method='add',cex=0)
rm(a)
a<-plotSmear(PScounts[,c(2,5)],  
panel.first=grid(),smooth.scatter=FALSE,main='Patient2 before',  
de.tags=deg.tgw, ylim=c(-10,10))
ma.plot(a$A,a$M,plot.method='add',cex=0)
rm(a)
a<-plotSmear(PScounts[,c(3,6)],  
panel.first=grid(),smooth.scatter=FALSE,main='Patient3 before',  
de.tags=deg.tgw, ylim=c(-10,10))
ma.plot(a$A,a$M,plot.method='add',cex=0)
rm(a)
b<-plotSmear(d.PS[,c(1,4)],  
panel.first=grid(),smooth.scatter=FALSE,main='Patient1 after',  
de.tags=deg.tgw, ylim=c(-10,10))
ma.plot(b$A,b$M,plot.method='add',cex=0)
rm(b)
b<-plotSmear(d.PS[,c(2,5)],  
panel.first=grid(),smooth.scatter=FALSE,main='Patient2 after',  
de.tags=deg.tgw, ylim=c(-10,10))
ma.plot(b$A,b$M,plot.method='add',cex=0)
rm(b)
b<-plotSmear(d.PS[,c(3,6)],  
panel.first=grid(),smooth.scatter=FALSE,main='Patient3 after',  
de.tags=deg.tgw, ylim=c(-10,10))
ma.plot(b$A,b$M,plot.method='add',cex=0)
rm(b)
dev.off()


On a completely unrelated note, I had saw the video already, hilarious
  It's a pilot to see if we want to move to this technology right now,  
so it's difficult to convince my bio-collaborator to go beyond 3 right  
now. So far, some important genes that we do not find in affy arrays  
because low intensity, also came with zero counts here, even though we  
used 80bp. And it's great for me to get familiar with teh analysis  
issues, as ou can see...


Mayte Suarez-Farinas
Research Associate, The Rockefeller University
Biostatistician, The Rockefeller University Hospital
1230 York Ave, Box 178,
New York, NY, 10065
+1(212) 327-8213



On Nov 24, 2010, at 7:50 PM, bioc-sig-sequencing-request at r-project.org  
wrote:

>
> Dear Mayte
>
> How do these plots look like when you make them separately for each
> subject? (In addition, you could colour the dots according to whether
> the differential expression analysis for the overall dataset calls  
> them
> 'significant').
>
> Also, if you compute the M values for each patient separately, how  
> does
> the pairs plot (scatterplot matrix) look like?
>
> On a completely unrelated note, I recently saw a movie about studies
> with 3 patients: http://www.xtranormal.com/watch/6878253
>
>        Best wishes
>        Wolfgang

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