[BioC] read.ilmn() and variation between chips

[Rao,Xiayu]

Hello, Gordon

Thanks a lot for answering my two questions. The information you provided was very important to us.

One quick question, you said that read.ilmn() reads the files as output by Genome Studio without any need for intermediate processing. What if I have so many samples from several chips, and I read in the data from each chip using read.ilmn(), and then I want to do comparisons based on all the samples? How to combine them?

Really appreciate your kind help!

Thanks,
Xiayu  



-----Original Message-----
From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU] 
Sent: Friday, June 28, 2013 2:37 AM
To: Rao,Xiayu
Cc: Bioconductor mailing list
Subject: read.ilmn() and variation between chips

Dear Xiayu,

Yes, it is good enough.  neqc() has done between-array normalization already, and there is no need for within-array normalization for Illumina BeadChips.

Please look at the help page

   ?neqc

The read stages that you describe sound complicated.  read.ilmn() reads the files as output by Genome Studio at our core facility without any need for intermediate processing.

Best wishes
Gordon

-------------------- original message -------------------- [BioC] read.ilmn() and variation between chips Rao,Xiayu XRao at mdanderson.org Wed Jun 26 20:08:09 CEST 2013

Hello,

I have a question about background correction and normalization. Please help me out! Thank you for your time!

I have four chips of microarray experiments, and therefore four data sets. 
I combined them together by merging on ProbeID and read in them as one file using read.ilmn(), and I combined all the control probe files into one and read it in. I just followed the limma user guide and use neqc() for background correction and normalization. Is it good enough? Do I need to consider within array and between array normalization?

Thanks,
Xiayu

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