Question: differential expression analysis of microarray data using limma package
0
gravatar for au.rinki.bio
15 months ago by
au.rinki.bio10
au.rinki.bio10 wrote:

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<-ReadAffy(cdfname='ovarian')
 data1
Data <- ReadAffy()
  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()
  genes<- topTable(fit, coef=2, n=40, adjust="BH")
   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 • 817 views
ADD COMMENTlink modified 15 months ago by WouterDeCoster40k • written 15 months ago by au.rinki.bio10

I added code markup to your post for increased readability. You can do this by selecting the text and clicking the 101010 button. When you compose or edit a post that button is in your toolbar, see image below:

101010 Button

ADD REPLYlink written 15 months ago by WouterDeCoster40k
2
gravatar for Corentin
15 months ago by
Corentin310
Corentin310 wrote:

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")
data1<-ReadAffy(cdfname='ovarian') 
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) 

 genes<- topTable(fit, coef=2, n=40, adjust="BH") 
 write.table(genes,"limma analyzed data1.xls",sep="\t",col.names = NA)
ADD COMMENTlink written 15 months ago by Corentin310

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.

ADD REPLYlink written 15 months ago by au.rinki.bio10

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.

ADD REPLYlink written 15 months ago by genomax69k
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