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@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@adelaide.edu.au > >http://mag.maths.adelaide.edu.au/crwilkinson.html > > > >>From: Yi Zou <yzou1971@netscape.net> >>Subject: [BioC] Confusion for choosing package for microarray data >> analysis >>To: bioconductor@stat.math.ethz.ch >>Message-ID: <40563DE5.1050000@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 >> >> > > > [[alternative HTML version deleted]]