I'd suggest using either limma or the marray packages for reading in
normalising and visualising. There is a lot of overlap between these
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
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
together. I think both the marray packages and limma were developed
sma as a basis.
I use limma since I'm interested in using the linear modelling
think it has good help, and is undergoing active development. However
occasionally use functions in the marray packages, or more often write
own code to do what I want.
As you get started I'd suggest that its well worth having a good look
documentation and browsing through the package description. When you
the help (html help menu in windows RGui or start.help() in linux)
package section and click on limma.
Read the overview carefully which explains how to do various things in
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
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
Both limma and marray packages contain references to papers regarding
normalisation and statistical issues in microarray analysis which you
also want to look at.
You might also want to have a look at
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
72 King William Rd, North Adelaide, 5006 | The University of Adelaide,
Math's Office (Room 121) Ph: 8303 3714
CHRI Office (CR2 52A) Ph: 8161 6363
> From: Yi Zou <firstname.lastname@example.org>
> Subject: [BioC] Confusion for choosing package for microarray data
> To: email@example.com
> Message-ID: <40563DE5.firstname.lastname@example.org>
> 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
> few days experience, I found there are so many different packages
> available like marray, limma, sma, com.braju.sma, siggenes...etc.
> of them have many functions doing the same work, and definitely each
> package may provide its specific functions. So, I'm a little
> for choosing these packages firstly for my following analysis.
> experienced members can give me a short answer for these two
> 1) Do I have to use these packages together, or just choose one that
> feel comfortable?
> 2) Can somebody give me a short comparison for those most commonly
> packages like limma, marray, sma..., especially give me a
> for those specific functions in a package which are not provided in
> other packages? Then I know which package I should choose at
> Any answers and suggestions is appreciated