Hello Everyone,
I am working on expression profiling of wheat, where five tissue
types were analyzed at anthesis (flowering) and 14 days after anthesis
(DAA14). Because of the amount of development that occurs during the
first two weeks of grain filling, we expect to see differentially
expressed genes (Code below). I approached this as a 5x2 factorial
problem and took my lead from the limma guide (ch 8.7). When calling
topTable for any contrast, the list of P.Values contains a range of
values (small small, some large). While the adj.P.Val column contains
the same (large) value for every gene reported ( up to n = 10,000).
The adj.P.Vals that comeback for each contrasts range from 0.87 to 0.9999..
Note the "ABSURD" contrast, when comparing different tissue types, we
expect a lot of DE genes, but still no DE reported
I subsetted my data to only analyze one tissue across the two times,
and then doing the analysis as below, and if it is approached it as a
'comparison of two groups', and still was returned adjusted p values
that were all the same, high value.
Any idea why my q-values are all the same?
################################################
library(limma)
library(affy)
#read in our data
target <- readTargets("--------/TargetCEL.txt")
data <- ReadAffy(filenames = target$FileName)
eset <- rma(data)
#Building our model. Let us model this as a 5x2 factorial problem
(five tissues, two times)
factors <- paste(target$Timing, target$Sample, sep = ".")
factors <- factor(factors, levels = c("ANTH.SPIKE", "ANTH.PEDUNCLE",
"ANTH.STEM", "ANTH.FLAGLEAF", "ANTH.NODES", "DAA14.SPIKE",
"DAA14.PEDUNCLE", "DAA14.STEM", "DAA14.FLAGLEAF", "DAA14.NODES"))
design <- model.matrix(~0+factors)
colnames(design) <- levels(factors)
contrastMat.TissueWise <- makeContrasts(
Spike = DAA14.SPIKE - ANTH.SPIKE,
PEDUNCLE = DAA14.PEDUNCLE - ANTH.PEDUNCLE,
STEM = DAA14.STEM - ANTH.STEM,
FLAGLEAF = DAA14.FLAGLEAF - ANTH.FLAGLEAF,
NODES = DAA14.NODES - ANTH.NODES,
ABSURD = ANTH.STEM - DAA14.SPIKE,
levels = design
)
fit3 <- lmFit(eset, design)
fit2 <- contrasts.fit(fit3, contrastMat.TissueWise)
fit <- eBayes(fit2)
topTable(fit2, coef = ___, n = 100)
--
Sam McInturf