[BioC] where to start?

michael watson (IAH-C) michael.watson at bbsrc.ac.uk
Wed Apr 20 13:22:41 CEST 2005

Hi Malik

What do you want to do?  Bioconductor has many packages which you could
use.  My preference when it comes to detecting differential expression
is for limma, but there are many others such as siggenes, genefilter,
multtest etc

For multtest, read the multtest.pdf after installing that library.  You
will want to read your data into an R data frame using read.table().

For limma, again you will probably need to read your data in using
read.table().  Then you can either create an exprSet class (type
?exprSet) or an MAList (documented in the limma help).  The limma
package is easier to use if you have the original data outputs from your
image analysis software....

For clustering, there are plenty of mails on this list that have dealt
with this, but the functions you may want to start with are hclust(),
dist() and cor(). 

But really, that's all just for starters!  What do you want to do?? :-)


-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Malik
Sent: 20 April 2005 06:57
To: bioconductor at stat.math.ethz.ch
Cc: yousef at wistar.org
Subject: [BioC] where to start?


I have a gene expression data set build up form rows of genes expression

GeneID  GeneName      Sample1    .......... Samplen

 Category                      +1      ...........-1

 1             gene1            0.5 ..............0.67

 2             gene2            0.34 ............. 0.78


How I could use bioconductor to analyze this data set and get the most
informative genes, classification.. Clustering and etc





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