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GAO Yu
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10
@gao-yu-3961
Last seen 10.6 years ago
Dear all!
When I use LIMMA to analyze microarray data, I use Array Weight
parameter. I
have some questions about it.
The command is:
#array weight
arrayw <- arrayWeights(MAbe, design, weights=MAbe$weights)
arrayweights <- targets
for (m in 1:narrays){arrayweights$arrayw[m] <- arrayw[[m]]}
write.table(arrayweights, file = file.path(resultdir,
"arrayweights_BewteenArray.txt"),sep="\t", dec = ".",row.names= FALSE)
# combine array and spot weights
library(statmod)
W <- matvec(MAbe$weights, arrayw)
# Fit linear models
fit <- lmFit(MAbe, design, method="ls", weights=W)
# Compute contrast of interest
cont.matrix <- makeContrasts (HvsL=L-H, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes (fit2)
# Toptable, remember to change name of generated files according to
contrast
toptable <- topTable(fit2,number=10000, adjust.method = "fdr")
DE <- toptable$adj.P.Val[toptable$adj.P.Val<0.05]
length(DE)
# 2285
toptable <- topTable(fit2, number=2285, adjust.method = "fdr",
sort.by="p")
write.table(toptable, file = file.path( resultdir,
"2285_between_ArrayWeight_fdr_CD8.txt"),sep="\t", dec = ".",row.names=
FALSE)
My questions are:
(1) How is Log2(Fold Change) in the toptable gotten?
If I don't use array weight, I can get log2(fold change) by
(M(l)-M(h)). But
when I use array weight, I don't know how to get it?
(2) How to get the M value that is used in lmFit analysis?
I know how to get the M value after normalization. I use this
command.
#loess normalize in arrays
MAin <- normalizeWithinArrays(RG, method="loess", weights=RG$weights,
bc.method="none")
# write normalised data to TMEV-file - for clustering
# Tab Delimited, Multiple Sample Files (TDMS) (*.txt)
mMin <- MAin$M
colnames(mMin) <- paste(colnames(mMin),targets$Pig,sep=".")
clusterdata <- cbind(MAin$genes,mMin)
FnameOutput <- file.path(resultdir,"Index_MAin_slides.txt")
write.table(clusterdata, file=FnameOutput, sep="\t", row.names=FALSE)
When I use array weight, I guess M value will be changed. Is it
that?
Thanks a lot for your attention!
Best wishes,
Yu GAO
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