[BioC] questions about Affy package from new user

[Lizhe Xu]

I started to use Bioconductor recently and had several questions about the Affy package. Please help me and even answer to one question will be appreciated. I know some question may take la ong paragraph to answer. 
(1) is it possible to do the summary first followed by normalization on probe set level with Affy?
(2) what is the advantage to do normailization first, then probe set summary compared to normalization at probe set level?
(3) After running bgcorrect, normalization and summary on probe set in Affy (expresso function), I want to export the probe set data and analyze it with GS (is there another package can do the same job as GS in bioconductor)? Should I do the per chip normalization again in GS?
Thanks.
 
Lizhe

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	From: bioconductor-request at stat.math.ethz.ch [mailto:bioconductor-request at stat.math.ethz.ch] 
	Sent: Sun 3/14/2004 5:04 AM 
	To: bioconductor at stat.math.ethz.ch 
	Cc: 
	Subject: Bioconductor Digest, Vol 13, Issue 26
	
	

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	Today's Topics:
	
	   1. LC-MS data (Nicholas Lewin-Koh)
	
	
	----------------------------------------------------------------------
	
	Message: 1
	Date: Sat, 13 Mar 2004 22:48:50 +0800
	From: "Nicholas Lewin-Koh" <nikko at hailmail.net>
	Subject: [BioC] LC-MS data
	To: bioconductor at stat.math.ethz.ch, S.Nyangoma at cs.rug.nl
	Message-ID: <1079189330.2264.182616685 at webmail.messagingengine.com>
	Content-Type: text/plain; charset="ISO-8859-1"
	
	Hi,
	To my knowledge there are only 2 packages in R specifically for MS data,
	mscalib on CRAN, and PROcess in bioconductor devel. The first is for
	MALDI tof spectrometers and assumes you have picked peaks already and
	works on the peaks list. The second is for seldi, but the baseline
	correction and peak picking are pretty generic. To process LC-MS data you
	have to decide how far back in the device internal processing you want to
	go. Personally, I have found that the mantra for mass spec data at the
	moment is "Don't trust vendor software". It mostly sucks. If you can get
	it you want to be grabbing the data stream as it is read of the column by
	the sensor, because it helps to warp the chromatagram from each scan so
	that the peaks align properly. Then you want to conver to m/z. After that
	comes all the signal processing song and dance, to subtract the chemical
	noise, make a baseline adjustment, etc. The tools for this in R are here
	and there and development for processing this stuff is nacent. There is
	much more available in matlab, which though much more expensive is mostly
	faster than R. The signal processing community and the chemometrics
	people tend to work in matlab.
	
	
	Note that it has been my experience that automated peak detection is an
	art, with more pitfalls than clustering. If you can do anything to avoid
	that using prior knowledge it helps. Good luck.
	
	Nicholas
	>
	> Message: 2
	> Date: 12 Mar 2004 19:12:32 +0100
	> From: Stephen Nyangoma <S.Nyangoma at cs.rug.nl>
	> Subject: [BioC] LC-MS proteomics data
	> To: bioconductor at stat.math.ethz.ch
	> Message-ID: <1079115152.10700.12.camel at iwi142>
	> Content-Type: text/plain
	>
	> Sorry for bothering you with this question.
	>
	> Has someone analylsed LC-MS data? How do you read this data into R? Are
	> there preprocessing tools in R? What are the crusial preprocessing
	> steps? Do the ascii files obtained from Brucker software contain raw
	> files? Thanks. Stephen.
	>
	> 
	>
	>
	>
	
	
	
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