questions about Affy package from new user
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Lizhe Xu ▴ 210
@lizhe-xu-666
Last seen 8.1 years ago
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 -----Original Message----- From: bioconductor-request@stat.math.ethz.ch [mailto :bioconductor-request@stat.math.ethz.ch] Sent: Sun 3/14/2004 5:04 AM To: bioconductor@stat.math.ethz.ch Cc: Subject: Bioconductor Digest, Vol 13, Issue 26 Send Bioconductor mailing list submissions to bioconductor@stat.math.ethz.ch To subscribe or unsubscribe via the World Wide Web, visit https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor or, via email, send a message with subject or body 'help' to bioconductor-request@stat.math.ethz.ch You can reach the person managing the list at bioconductor-owner@stat.math.ethz.ch When replying, please edit your Subject line so it is more specific than "Re: Contents of Bioconductor digest..." 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@hailmail.net> Subject: [BioC] LC-MS data To: bioconductor@stat.math.ethz.ch, S.Nyangoma@cs.rug.nl Message-ID: <1079189330.2264.182616685@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@cs.rug.nl> > Subject: [BioC] LC-MS proteomics data > To: bioconductor@stat.math.ethz.ch > Message-ID: <1079115152.10700.12.camel@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. > > > > > ------------------------------ _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor End of Bioconductor Digest, Vol 13, Issue 26 ******************************************** -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: application/ms-tnef Size: 7826 bytes Desc: not available Url : https://www.stat.math.ethz.ch/pipermail/bioconductor/attachments /20040314/372c2674/attachment.bin
Proteomics Normalization GO Preprocessing Clustering probe affy PROcess mscalib GO probe • 796 views
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@james-w-macdonald-5106
Last seen 6 hours ago
United States
1.) Once you have done the summary, you don't have probe set data anymore, so you cannot normalize on the probe set level. However, if you are doing the mas5 algorithm the normalization (such as it is) occurs after summarization. 2.) The normalization *is* done at the probe set level, so I don't understand the question. 3.) What's GS? Well, regardless, you probably would not want to re-normalize the data. HTH, Jim James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 >>> "Lizhe Xu" <lxu@chnola-research.org> 03/14/04 06:00PM >>> 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 -----Original Message----- From: bioconductor-request@stat.math.ethz.ch [mailto:bioconductor-request@stat.math.ethz.ch] Sent: Sun 3/14/2004 5:04 AM To: bioconductor@stat.math.ethz.ch Cc: Subject: Bioconductor Digest, Vol 13, Issue 26 Send Bioconductor mailing list submissions to bioconductor@stat.math.ethz.ch To subscribe or unsubscribe via the World Wide Web, visit https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor or, via email, send a message with subject or body 'help' to bioconductor-request@stat.math.ethz.ch You can reach the person managing the list at bioconductor-owner@stat.math.ethz.ch When replying, please edit your Subject line so it is more specific than "Re: Contents of Bioconductor digest..." 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@hailmail.net> Subject: [BioC] LC-MS data To: bioconductor@stat.math.ethz.ch, S.Nyangoma@cs.rug.nl Message-ID: <1079189330.2264.182616685@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@cs.rug.nl> > Subject: [BioC] LC-MS proteomics data > To: bioconductor@stat.math.ethz.ch > Message-ID: <1079115152.10700.12.camel@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. > > > > > ------------------------------ _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor End of Bioconductor Digest, Vol 13, Issue 26 ********************************************
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