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, [email protected]
<mailto:[email protected]> 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
