differential expression analysis of microarray data using limma package
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3.1 years ago
au.rinki.bio ▴ 10

hello, i am working on microarray data analysis using R/Bioconductor package. for differential expression analysis i am using limma package. i am consedering cel file. my data containing 4 sample for normal, and 8 sample for disease data . i am using following command line for analysis.

 library(limma)
library(affy)
library(makecdfenv)
library(affyio)
library(affy)
ovarian<-make.cdf.env("HGU133A_Hs_ENSG.cdf")
data1
eset <- rma(data1)
ph<-pData(eset)
ph
p_disease<- c("control","control","control","control","case","case","case","case","case","case","case","case")
p_disease
design <- model.matrix(~factor(p_disease))
colnames(design) <- c("case","control")
design
fit <- lmFit(eset, design)
fit <- eBayes(fit)
options(digits=2)
getwd()
write.table(genes,"limma analyzed data1.xls",sep="\t",col.names = NA)


is am i in write direction ? kindly give me your valuable suggestion. thanking you

microarray data analysis • 2.0k views
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3.1 years ago
Corentin ▴ 460

Hi,

It seems alright,

I think it would be informative to add some quality control at the beginning, you can do boxplots of the raw intensity to compare different cells, and check the RNA degradation with "AffyRNAdeg".

If the quality seems not good enough for your analysis, you can them perform normalization. Then, you can redo the boxplot to check which nomalization method perform best for your data.

If you need more information, the limma user guide is well detailed, https://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf

When you ask a question, you have to format your code with the tags "block quote", it will be easier for us to read, like this:

library(limma)
library(affy)
library(makecdfenv)
library(affyio)
library(affy)

ovarian<-make.cdf.env("HGU133A_Hs_ENSG.cdf")
eset <- rma(data1)
ph<-pData(eset)
ph p_disease<- c("control","control","control","control","case","case","case","case","case","case","case","case")

p_disease design <- model.matrix(~factor(p_disease))
colnames(design) <- c("case","control")

fit <- lmFit(eset, design)
fit <- eBayes(fit)
options(digits=2)

write.table(genes,"limma analyzed data1.xls",sep="\t",col.names = NA)

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thankyou for suggestion. if you look code carefully, i were not generate phase contrast matrix. i am confused when genereting this, because i have 4 sample for normal with different GSM number, 8 sample for disease with different GSM number.
if you understand my question plz suggest me command line for this.

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It sounds like this question has not been answered so @Corentin's answer should not be accepted. I am going to reset the toggle on that.