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

[Rao,Xiayu]

Dr. Smyth,

Yes, you are right. I just understand what you meant. I should have thought more and been more careful. Thank you very much!

The limma package your research group developed really help us out to analyze microarray data and other data.

Thanks,
Xiayu

-----Original Message-----
From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU] 
Sent: Wednesday, July 03, 2013 4:11 AM
To: Rao,Xiayu
Cc: Bioconductor mailing list
Subject: RE: read.ilmn() and variation between chips

On Tue, 2 Jul 2013, Rao,Xiayu wrote:

> Dr. Smyth,
>
> Thanks a lot for your important message! I did read your limma user 
> guide, and only found that "If there are multiple probe summary 
> profiles to be read, and the samples are summarized in a targets 
> frame, then the read.ilmn.targets function can be used." When I typed 
> ?read.ilmn.targets in R, not much syntax showing up. I also read your 
> paper: Optimizing the noise versus bias trade-off for Illumina Whole 
> Genome Expression BeadChips. Nucleic Acids Research 38, e204. But I 
> did not find an example for that. Could you please let me know how 
> read.ilmn() reads multiple files and collate them.

Thanks for explaining the documentation you have read, but I had thought the natural place to start would be the help page for read.ilmn(), which you can access by ?read.ilmn.  This tells you that 'files' can be a vector of file names.

Best wishes
Gordon

> For a beginner in microarray data analysis, it is so great to have 
> your help!!! Really appreciate it!
>
> Thanks,
> Xiayu
>
>
> -----Original Message-----
> From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU]
> Sent: Friday, June 28, 2013 6:46 PM
> To: Rao,Xiayu
> Cc: Bioconductor mailing list
> Subject: RE: read.ilmn() and variation between chips
>
> Dear Xiayu,
>
> Genome Studio normally exports multiple BeadChips to the same probe 
> summary profile file.  Our core centre normally exports all the chips 
> for each experiment to the same file.  Even if you do have multiple 
> files, read.ilmn() will read multiple files and collate them for you.
>
> Have you read the documentation?
>
> Best wishes
> Gordon
>
> On Fri, 28 Jun 2013, Rao,Xiayu wrote:
>
>> 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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