microarray data analysis

Question

[au.rinki.bio]

I am working on microarray data analysis. to find out DE gene i have use p value <0.05. now i want to go with Benjamini-Hochberg correction, for this i have use samr package in R. for filtering significant genes delta value must be given by users. plz suggest me range of delta value and also give information about what is q value? how it is useful for Differential expression analysis of gene?

[russhh]

If you're working in R, are you using limma? and if so, why didn't you filter based on adjusted-p-value (the BH correction will have been done for you) instead of p-value.

[WouterDeCoster]

Hi au.rinki.bio,

Please chose a more descriptive title of your threads, "microarray data analysis" doesn't say a lot. "Multiple testing correction" is something you should have included, for example.

In addition, please give feedback on your previous question(s):
If an answer was helpful you should upvote it, if the answer resolved your question you should mark it as accepted.

Cheers,
Wouter

[au.rinki.bio]

thank you. i would follow your sugestion.

Answer 1

[arta]

The reason why you are correcting the p values is multiple testing. In microarray data there are around 20.000 genes, as rows. So you do 20.000 times testing whether the gene is significantly DE. When you set your p value to 0.05, 1000 genes (20000 * 0.05) are significantly differentially expressed by chance. To avoid getting false positives or type-1 error, you correct your p values. Corrected p-values are generally called adjusted p values and q value is p value which is adjusted for False Discovery Rate (FDR).

To do that in R, you do not need to use any extra package. Following function will do the same job.

p.adjust(p.values, method = "BH", n = length(p.values))

[au.rinki.bio]

i am using following command line and use affy package.

library(makecdfenv)
library(affyio)
library(affy)
ovarian<-make.cdf.env("HGU133A_Hs_ENSG.cdf")
data<-ReadAffy(cdfname='ovarian')
data
eset<-rma(data)
exprSet <- exprs(eset)
pvalue.exprSet = apply(exprSet, 1, function(x){t.test(x[1:4], x[5:45]) $p.value}) 
Combine.exprset.pvalue = cbind(exprSet,pvalue.exprSet)
write.table(Combine.exprset.pvalue,"NormalizedValues.xls",sep="\t",col.names = NA)

[WouterDeCoster]

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:

[au.rinki.bio]

when i am using command suggested by you after p value calculation following error occur-

p.adjust(p.values, method = "BH", n = length(p.values))
Error in p.adjust(p.values, method = "BH", n = length(p.values)) : 
  object 'p.values' not found
Error during wrapup: cannot open the connection

[au.rinki.bio]

thank you so much. but here is one more question what should be the range of thresold for agjusted p -value?

[russhh]

Based on your text above, the threshold should be 0.05. Can I strongly suggest that you read and work through the limma user's guide rather than doing the unmoderated t-tests that you've proposed. And by strongly suggest, I mean, really strongly suggest.

[au.rinki.bio]

thank you so much for suggestion.

[au.rinki.bio]

when i am using limma package their are a lots of error coming and not running proprely. kindly suggest me any tutorial for analysis of microarray data( CEL file) using limma package.

[russhh]

In what way did it fail, and what have you done to work out why it failed? Here's a brusque tutorial I wrote: here

[au.rinki.bio]

thank you so much. but here is one more question what should be the range of thresold for agjusted p -value?