Entering edit mode
Lizhe Xu
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210
@lizhe-xu-666
Last seen 10.2 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
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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@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.
>
>
>
>
>
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