[BioC] some help requested for constructing an appropriate design matrix in LIMMA

[Belinda Phipson]

Hi Steven

You could just include cell line in your linear model rather than using
duplicateCorrelation().

> design <- model.matrix(~factor(targets$cellline)+factor(targets$fenotype))
> fit <- lmFit(eset,design)
> fit <- eBayes(fit)

This will test R vs S taking into account cell line. You could also filter
out lowly expressed genes across all samples to improve your power to detect
differentially expressed genes as your sample size is quite small.

Cheers,
Belinda

-----Original Message-----
From: bioconductor-bounces at r-project.org
[mailto:bioconductor-bounces at r-project.org] On Behalf Of steven segbroek
Sent: Friday, 15 June 2012 2:13 AM
To: bioconductor at r-project.org
Subject: [BioC] some help requested for constructing an appropriate design
matrix in LIMMA

Dear R-users,

I want to analyse a single channel micro array experiment which looks like
the following:

> targets
  File cellline fenotype
1    A       1        R
2    B       2        R
3    C       3        R
4    D       1        S
5    E       2        S
6    F       3        S

There are three different cell lines, each of which comes in two versions.

Every cell line has  a variant that is resistant to a specific drug and
another variant that is sensitive to this drug.

We treated both variant of the three cell lines with this drug and then
extracted RNA which was then hybridised to a micro array.

The question we want to resolve is: which genes are differentially
regulated between resistant (R) and sensitive (S) versions of these cell
lines.

There is quite some biological variation between the cell lines, so
grouping them by fenotype and then searching for differentially regulated
genes would be a bad idea.

So, the idea is to to construct a model that accounts for this biological
variation between the cell lines and looks which genes are consistently  up
or down regulated between resistant and sensitive versions of these three
cell lines.

I am a bit puzzled on how to setup an appropriate design matrix for this
particular setup.

I have come up with the following code:
> design
  celline fenotR fenotS
1       1      1      0
2       2      1      0
3       3      1      0
4       1      0      1
5       2      0      1
6       3      0      1
attr(,"assign")
[1] 1 2 2
attr(,"contrasts")
attr(,"contrasts")$fenot
[1] "contr.treatment"

>block<-c(1,2,3,1,2,3)
>eset<-exprs(BSData.log2.quantile)
>cor<-duplicateCorrelation(eset, ndups=1, block=block, design=design)
>fit <- lmFit(eset, design, block=block, cor=cor$consensus)
>fit<- eBayes(fit)
>cont.matrix<-makeContrasts(resvssens= fenotR - fenotS, levels=design)
>fit2<-contrasts.fit(fit,cont.matrix)
>fit2<-eBayes(fit2)
>topTable(fit2)

However, this code results in an adj.p-value that is "0.9999541" for every
gene.

Is there a better way to analyse this?

Kind regards,
Steven

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