limma tutorial for microarray data analysis
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Entering edit mode
@aurinkibio-14614
Last seen 5.9 years ago

 hello, i am working on differential expression analysis of  cancer microarray data using R/ Bioconductor. my data have 4 sample for normal tissue and 4 for cancerous tissues. i got anonimous result that is Down regulated genes are 20104  no genes are upregulated . where i went wrong kindly suggest me. i am using following code in r  

 library(limma)
library(makecdfenv)
ovarian<-make.cdf.env("HGU133Plus2_Hs_ENSG.cdf")
abatch<-ReadAffy(cdfname='ovarian')
abatch
eset <- rma(abatch)
dh<-pData(abatch)
dh
disease<- c("normal","normal","normal","normal","cancer","cancer","cancer","cancer")
disease
design <- model.matrix(~factor(disease))
design
d<-colnames(design) <- c("normal","cancer")
d
fit <- lmFit(eset, design)
fit
names(fit)
cont.matrix <- makeContrasts(cancer-normal,levels=design)
cont.matrix
fit2  <- contrasts.fit(fit, cont.matrix)
fit2  <- eBayes(fit2)
colnames(fit2)
topTable(fit2,coef=1)
topTable(fit2,coef=1,adjust="fdr")
results <- decideTests(fit2)
results
summary(results)
vennDiagram(results)
output-                                                                                                                                                                                                                    result summary

 summary(results)
       cancer - normal
Down             20104
NotSig              14
Up                   0                                                                                      
microarray • 595 views
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Entering edit mode
@gordon-smyth
Last seen 39 minutes ago
WEHI, Melbourne, Australia

I think you intended

design <- model.matrix(~ 0 + factor(disease))

If you add "0+" your code will work correctly.

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