[BioC] Question about amount of diff expressed genes in fit using a design matrix with intercept
Belisa Santos [guest]
guest at bioconductor.org
Sat Nov 24 15:23:12 CET 2012
Hello everybody,
I have used a design matrix with a control as intercept. When I fit the matrix with my RMA normalized expression set, do eBayes, and then ask for a topTable:
topTable(fit, adjust="BH", p.value=1e-5, lfc=1, number=Inf)
I get the same number of genes as I have input (54675)... and the lowest adjusted p-value is e-29... Now it cannot be the case where ALL my genes are differentially expressed between control and treatments....
I am missing something? The intercept functions as a "contrast", right? Is this common? Could I be doing something wrong? Please help me...
Thank you in advance for you kind help. All the best,
Belisa
-- output of sessionInfo():
# Code I am using,
cel<-ReadAffy
expression_set <- rma(cel)
fit <- lmFit(expression_set, design.ctrl_intercept)
fit <- eBayes(fit)
topTable(fit, adjust.method="BH", p.value=1e-5, lfc=1, number=Inf)
# My design matrix
ctrl0 B1.ctrl BT1.ctrl BI1.ctrl BTI1.ctrl B2.ctrl BT2.ctrl BI2.ctrl BTI2.ctrl B3.ctrl BT3.ctrl BI3.ctrl BTI3.ctrl B7.ctrl BT7.ctrl BI7.ctrl BTI7.ctrl
1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
5 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
10 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
11 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
12 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
13 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
14 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
15 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
(...)
52 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
53 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
54 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
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