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mrjmorri@ucalgary.ca
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40
@mrjmorriucalgaryca-4873
Last seen 9.6 years ago
Hello,
My name is Matthew Morris, I'm a second year Master's student at the
University of Calgary, and I am fairly new to the world of
microarrays!
My project involves single-colour arrays, for which helpful documents
seem
to be rather limited.
As such, I have some questions. I'll ask the questions first, and
then
show you the code. If you can only answer one of the three, some
info is
better than none!
1. How do I visualize my data pre- and post-normalization? What will
I be
looking for?
2. I am using code from someone else to flag nonuniform and feature
population outliers. It certainly alters my results, but I'm not
sure how
to check if it is working correctly.
3. How can I incorporate things like sex and length into my model? (to
clarify, I am looking at four populations of fish called Cran, Hog,
OL and
LCM respectively, raised at 7 or 23 degrees, and I would like to
eliminate
the effects of sex, tank and length)
Thank you very much,
Matthew Morris
Code is as follows:
library(limma)
#read Targets file (make sure to set directory first through File)
targets<-readTargets("targets.txt")
#checks that file was read correctly
targets$FileName
#weight OL
wtAgilent.mRGOLFilter <- function(qta)
{mapply(min,1-qta[,"gIsFeatNonUnifOL"],1-qta[,"gIsFeatNonUnifOL"],1-qt
a[,"gIsFeatPopnOL"],1-qta[,"gIsFeatPopnOL"])}
#read data from array ouput files into E
E<-read.maimages(targets$FileName,
source="agilent.median",path="actualall",
wt.fun=wtAgilent.mRGOLFilter,
columns=list(E="gProcessedSignal"),
other.columns=list(saturated="gIsSaturated",
nonuniform="gIsFeatNonUnifOL", popnoutlier="gIsFeatPopnOL",
flag="IsManualFlag", wellabovebg="gIsWellAboveBG"))
#to see that everything looks fine
E
#normalizing between arrays:
normalize<-normalizeBetweenArrays(E, method="quantile")
#remove control spots
dat1<-normalize[normalize$genes$ControlType==0,]
#average values for identical probes within an array
Eavg<-avereps(dat1, ID=dat1$genes$ProbeName)
#analyze factorial design: first identify the factors in template
TS <- paste(targets$Population, targets$Temperature, sep=".")
#check to see it worked
TS
#set up design
TS <- factor(TS, levels=c("Hog.7","Cran.7","LCM.7","OL.7", "Hog.23",
"Cran.23", "OL.23", "LCM.23", "Hog.15"))
design <- model.matrix(~0+TS)
colnames(design) <- levels(TS)
fit <- lmFit(Eavg, design)
cont.matrix <- makeContrasts(Hog.7vs23=Hog.23-Hog.7,
Cran.7vs23=Cran.23-Cran.7, LCM.7vs23=LCM.23-LCM.7,
OL.7vs23=OL.23-OL.7,
levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)
#check results
topTable(fit2, coef=1, adjust="BH")
results <- decideTests(fit2)
vennCounts(results)
vennDiagram(results)
write.table (fit2, file="fit2.txt")
