Entering edit mode
Sunny Srivastava
▴
350
@sunny-srivastava-3793
Last seen 10.3 years ago
Hello Bioconductor Gurus,
I have the a data about gene expression from TWO COLORED Agilent
array. I
wanted to check differential expression between p3 and wild strain of
yeast.
In one array p3 is colored with Cy5 and wild is colored with Cy3 and
in the
second array the dyes are swapped. Assuming I have normalized my data
using
VSN and obtained M values for the two arrays, I now want to use limma
to
derive the differentially expressed genes.
My model matrix (say design) in this case will be
p3
1
-1
if wild type is the reference.
If my understanding is correct about how limma analyzes differential
expression, then M value is the dependent variable, sample annotation
(whether p3 or wild, provided by design) is the independent
(explanatory)
variable, and a linear model is fit per gene using the following
equation.
lmFit( M , design)
As the data per gene is small, it is better to use eBayes method to
obtain
genewise p-value. But the object obtained from eBayes (say fit3)
doesn't
contain the value *logFC*. When I use topTable to order the genes,
then
logFC appears.
The concept of logFC is clear to me in case of a Affy single colored
array
(ie log (Int_trt/ Int_control) ), but somehow I am still confused how
to
interpret this in two colored arrays.
In my opinion M value (for each array) should represent logFC if color
bias
is ignored. How does limma derives its logFC value in two colored
arrays? Is
it based on the B statistics? Please enlighten me !
Thanks in advance for any help.
Best Regards,
S.
[[alternative HTML version deleted]]