Dear Muralidharan V, See the case study in the limma User's Guide called "Section 11.8. Agilent Single-Channel Data: Gene expression in thymus from female Wistar rats". The description on GEO says that this is 8x60k data. As far as I know, there is no difference in the analysis steps for 4x44k or 8x60k arrays. Best wishes Gordon > Date: Fri, 27 Apr 2012 17:43:52 +0530 > From: Muralidharan V <muralidharanv89 at="" gmail.com=""> > To: bioconductor at r-project.org > Subject: [BioC] Filtering Codes > > Hai, > > I am using the data obtained from Agilent 8x60k chip for the analysis of > mRNA expression. I have gone through lots of papers that describes only > about Agi 4x44 chips. > > Here am facing a trouble of filtering the genes using the LIMMA package in > R+Bioconductor. What is the basic code implemented in doing this filtering > process? > > I just want to know how the unwanted ProbeID,s or genes, which are of > not importance, can be filtered out using the LIMMA package. > > The code for normalization that i am using is: > > *y <- normalizeBetweenArrays(y, method="quantile")* > * > * > * > * > Could you please help me by providing the code that can be used for the > filtering of 8x60k data using LIMMA package in R+Bioconductor? > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}