[BioC] limma duplicateCorrelation - unbalanced paired design
Sarah Bonnin
Sarah.Bonnin at crg.eu
Fri Nov 15 18:05:47 CET 2013
Dear all,
I have a question regarding duplicateCorrelation when applied to a paired design.
I am analyzing 8 arrays (Agilent gene expression): 2 experimental groups A and B, samples are paired with one another: A1 with B1, A2 with B2 etc.
Sample B2 has to be dropped because of quality issues, so one sample in group A missing its corresponding B.
I have first processed an unpaired analysis of the data before processing a paired analysis (given the information I accessed later on).
targets:
FileName Group Pairs
A1.txt A 1
A2.txt A 2
A3.txt A 3
A4.txt A 4
B1.txt B 1
B3.txt B 3
B4.txt B 4
## Code
design <- model.matrix(~0+targets$Group)
colnames(design) <- unique(targets$ Group)
# UNPAIRED ANALYSIS #
fit <- lmFit(expr, design=design)
contrast.matrix <- makeContrasts(contrasts="B-A", levels=design)
fit <- contrasts.fit(fit, contrast.matrix)
fit <- eBayes(fit)
top.unpaired <- topTable(fit, coef="B-A", number=nrow(expr), sort.by="none")
# PAIRED ANALYSIS #
corfit <- duplicateCorrelation(expr, design, block=targets$Pairs)
fit <- lmFit(expr, design=design, block = targets$Pairs, cor = corfit$consensus)
contrast.matrix <- makeContrasts(contrasts="B-A", levels=design)
fit <- contrasts.fit(fit, contrast.matrix)
fit <- eBayes(fit)
top.paired <- topTable(fit, coef="B-A", number=nrow(expr), sort.by="none")
## Results
top.unpaired[1,]
logFC AveExpr t P.Value adj.P.Val B
0.07913307 10.30572 0.7052201 0.49455794 0.9999397 -4.767979
top.paired[1,]
logFC AveExpr t P.Value adj.P.Val B
0.08180912 10.30572 0.779451 0.451223001 0.9996952 -4.769007
What I do not understand is why the two logFC for the same probe are not similar?
I can easily calculate logFC from the unpaired results, but not for the paired.
I guess the fact that the paired design is "unbalanced" should be the reason; when I processed paired and unpaired analysis before removing sample B2 - 4 samples per experimental group - the logFC are identical.
I would like to understand how it works: is there some weight applied to the samples in this case?
I would also like to make sure that I don't make any mistake processing a paired analysis on this unbalanced paired design: should I also drop sample A2??
Any help and explanation is welcome.
Thanks!
Sarah
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