Hi Steve,

This was the script I used-
getwd()
setwd(dir="/CRSP 406-11/DEMOS/GSE14905-a")
ls()
#-----------------------------------------------#
library(affy)
eset = justRMA()
f <- factor(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,

2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2),
labels=c("Healthy", "unaffected"))
design <- model.matrix(~ 0 + f)
design
colnames(design) <-c("Healthy","unaffected")
design
library(limma)
fit <- lmFit(eset, design)
library(hgu133plus2.db)
fit$genes$Symbol <- getSYMBOL(fit$genes$ID,"hgu133plus2.db")
contrast.matrix <-makeContrasts(affected-Healthy,levels = design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
topTable(fit2,coef=1,p=0.05, adjust="fdr")
results <- decideTests(fit2, adjust="fdr", p=0.05)
summary(results)
write.table(results,file="myresults.txt")
write.fit().

I had identified ~54,000 genes of which ~ 20K were differentially expressed.

But when I use these genes for pathway analysis the software asks for fold
change values but not p value so it is easier to analyze the data.

What I did was - I used the differentially expressed gene table for further
analysis. That is I converted logFC values to FC(test/control) assuming
that

FC= FCmean(test)-FCmean(blank) and LogFC is log2 of FC values.

Once I got test/control values I converted them to fold changes using "IF"
function in excel sheet to eliminate genes with fold changes between -2 to
+2.

Once I did this the number of significant genes drastically reduced to ~
2,000.

Is this the right method?

Please advice, thanks, Roopa

On Thu, Jan 31, 2013 at 3:23 PM, Steve Lianoglou <
mailinglist.honeypot@gmail.com> wrote:

> Hi,
>
> On Thu, Jan 31, 2013 at 2:54 PM, Roopa Subbaiaih <rss115@case.edu> wrote:
> > Hi All,
> >
> > Thanks for the reply, I could pull out the the whole information for
> > differentially expressed genes. The criteria used was adjust="fdr",
> p=0.05.
> > I came up with ~ 20,000 genes to be differentially expressed.
>
> Hmm ... surely 20k cannot be correct?
>
> > Since I wanted to analyze these genes for deregulated pathways I had to
> > come up with fold change values for further analysis.
> >
> > I assume that for each gene FC= FCmean(test)-FCmean(blank). LogFC is log2
> > of FC values.
> >
> > When I convert the FC values (test/blank) to foldchanges using IF
> function
> > I get lesser number of genes to be deregulated. The criteria was =>2
> > foldchanges and =<-2 fold changes.
>
> I'm missing previous context to this email, so -- not sure what the
> "IF function" is, but if you're using limma, the log2fold changes are
> reported for you in the logFC column that is returned from
> `topTable(...)`
>
> -steve
>
> --
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
>  | Memorial Sloan-Kettering Cancer Center
>  | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact
>



-- 
---------------------------------------
Roopa Shree Subbaiaih
Post Doctoral Fellow
Department of Dermatology
School of Medicine
Case Western Reserve University
Cleveland, OH-44106
Tel:+1 216 368 0211

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