[BioC] Model matrix used in analysis of Agilent single color data

[Ekta Jain]

Hello,
As far as i know, the groups in LIMMA for samples are not always down to the fact if they are replicates or  not. You label samples as, in this case, 1 or 2 depending on treatment or a condition (such as duration of treatment, cell lines etc). For example if you have 2 samples treated with DMSO and 2 treated with CPI, the design matrix can be of the type  design <-model.matrix(~0+factor(c(1, 1, 2, 2). If you have 2 DMSO samples coming from same cell line having 1 replicate each then the design matrix can be 
design <-model.matrix(~0+factor(c(1,-1,1,-1)

Hope this helps.
Ekta

-----Original Message-----
From: bioconductor-bounces at r-project.org [mailto:bioconductor-bounces at r-project.org] On Behalf Of Muralidharan V
Sent: 27 April 2012 11:46
To: bioconductor at r-project.org
Subject: [BioC] Model matrix used in analysis of Agilent single color data

Hai,

I just want to know the model that we have to use for the LIMMA package for
analysing the agilent single colour data. Currently am using the model:

*design <- model.matrix(~ 0+factor(c(1,1,2,2)))*

*colnames(design) <- c("group1", "group2")*

*fit <- lmFit(y.ave, design)*

*print(design)

*
Is this model design suits for analyzing the Agilent single color data
using the LIMMA package without having any replicates? Instead of using the
replicate of the samples we have just used the pooled samples in analyzing
the data.


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