Hello All,

I'm trying to understand the method of differential expression analysis
described in :
*
*
*Stem cell transcriptome profiling via massive-scale mRNA sequencing*
*Nicole Cloonan et al*
*NATURE METhODS | VOL.5 NO.7 | JULY 2008 | 613*
http://www.nature.com/nmeth/journal/v5/n7/abs/nmeth.1223.html
*Section: Statistical Analysis *
To calculate differential expression of SQRL tag data we analyzed the
normalized gene signals (tags per Refseq transcript, length-normalized) for
each library using the Limma package in R. After Quantile normalization, we
used Limma to fit a linear model to the log2-transformed data using an
empirical Bayes method32 to moderate standard errors. Differentially
expressed genes were defined as those with a B statistic > zero.

I have quantile normalized log2-transformed RPKM data and I wanted to find
DE genes based on B statistic and log2foldchange.
*Sample Rawdata:*

GeneModel CON1 CON2 TR1 TR2
1s00200.1 2.723945276 3.775939811 3.623211245 3.717795434
1s00210.1 4.999354495 3.738129253 3.268468778 3.822220668
1s00220.1 1.450861588 1.265013193 0.942706046 0.744551693

I'm using the following R-script:

library(limma)
raw.data <-
read.delim("INPUT-QuantileNormalizedLog2Transformed-RPKM-Data.txt")
attach(raw.data)
names(raw.data)
d <- raw.data[, 2:5]
rownames(d) <- raw.data[, 1]
Group <- factor(c("CON","CON","MYC","MYC"), levels=c("CON","MYC"))
design <- model.matrix(~0+Group)
colnames(design) <- c("CON","MYC")
fit <- lmFit(d, design)
fit <- eBayes(fit)
*Warning message:*
*Zero sample variances detected, have been offset*
topTable(fit,coef=2,number=1000,genelist=fit$genes)
 - THIS LISTS THE GENES ALL POSITIVE LOGFC VALUES - NON -NEGATIVE.

R version 2.14.0 (2011-10-31)
Platform: i386-apple-darwin9.8.0/i386 (32-bit)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] limma_3.10.0


Can someone please let me know what I'm doing wrong here. Is it in the
array design or input data?

Thank you,
Avinash

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