[BioC] Confusion for choosing package for microarray data analysis

[Yi Zou]

Hi Chris,

Thanks so much for you, Gordon, Sean and other member's patient answer, it's very clear to me know. Your suggestions is very useful to the R beginner like me. 

Cheers

Yi 

Christopher.Wilkinson at adelaide.edu.au wrote:

>Hi Yi,
>
>I'd suggest using either limma or the marray packages for reading in data,
>normalising and visualising. There is a lot of overlap between these two
>packages so either should be satisfactory. When it comes to assessing
>differentially expressed genes, you can use limma (linear modelling
>approach), siggenes (implements SAM) or whatever else you think is
>appropriate.
>
>My understanding is that sma was developed prior to Bioconductor (and
>com.braju.sma is a variant of sma developed using a different object
>oriented approach), and development of sma stopped when Bioconductor was put
>together. I think both the marray packages and limma were developed using
>sma as a basis.
>
>I use limma since I'm interested in using the linear modelling approach, I
>think it has good help, and is undergoing active development. However I
>occasionally use functions in the marray packages, or more often write my
>own code to do what I want.
>
>As you get started I'd suggest that its well worth having a good look at the
>documentation and browsing through the package description. When you start
>the help (html help menu in windows RGui or start.help() in linux) goto the
>package section and click on limma.
>
>Read the overview carefully which explains how to do various things in
>limma.
>Then look over the Introduction, classes, reading data, normalisation,
>linear models and diagnostics section. From there just browse through
>different functions to see what they do.  I also find its worth having a
>look through the R code for limma and marray packages. You can see how
>things are done and it gives you ideas on how to write your own functions.
>
>Both limma and marray packages contain references to papers regarding
>normalisation and statistical issues in microarray analysis which you might
>also want to look at.
>You might also want to have a look at
>http://www.statsci.org/micrarra/refs.html
>
>Cheers
>Chris
>
>Dr Chris Wilkinson
>
>Research Officer (Bioinformatics)        | Visiting Research Fellow
>Child Health Research Institute (CHRI)   | Microarray Analysis Group
>7th floor, Clarence Rieger Building      | Room 121
>Women's and Children's Hospital          | School of Applied Mathematics
>72 King William Rd, North Adelaide, 5006 | The University of Adelaide, 5005
>
>Math's Office (Room 121)        Ph: 8303 3714
>CHRI   Office (CR2 52A)         Ph: 8161 6363
>
>Christopher.Wilkinson at adelaide.edu.au
>
>http://mag.maths.adelaide.edu.au/crwilkinson.html
>
>  
>
>>From: Yi Zou <yzou1971 at netscape.net>
>>Subject: [BioC] Confusion for choosing package for microarray data
>>  analysis
>>To: bioconductor at stat.math.ethz.ch
>>Message-ID: <40563DE5.1050000 at netscape.net>
>>Content-Type: text/plain; charset=us-ascii; format=flowed
>>
>>Dear list members,
>>
>>I'm just starting using R to analyze my cDNA microarray data. After a
>>few days experience, I found there are so many different packages
>>available like marray, limma, sma, com.braju.sma, siggenes...etc. Some
>>of them have many functions doing the same work, and definitely each
>>package may provide its specific functions. So, I'm a little confused
>>for choosing these packages firstly for my following analysis. Hopefully
>>experienced members can give me a short answer for these two questions:
>>
>>1) Do I have to use these packages together, or just choose one that I
>>feel comfortable?
>>
>>2) Can somebody give me a short comparison for those most commonly used
>>packages like limma, marray, sma..., especially give me a introduction
>>for those specific functions in a package which are not provided in
>>other packages? Then I know which package I should choose at beginning.
>>
>>Any answers and suggestions is appreciated
>>
>>Yi
>>    
>>
>
>  
>

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