[BioC] using the Limma package with matrix data

[Paul Leo]

Think he probably mean you try 
> help("lmFit")
On the R-window console...
At the bottom of the help window generated you will see:
....


# Simulate gene expression data for 100 probes and 6 microarrays
# Microarray are in two groups
# First two probes are differentially expressed in second group
# Std deviations vary between genes with prior df=4
sd <- 0.3*sqrt(4/rchisq(100,df=4))
y <- matrix(rnorm(100*6,sd=sd),100,6)
rownames(y) <- paste("Gene",1:100)
y[1:2,4:6] <- y[1:2,4:6] + 2
design <- cbind(Grp1=1,Grp2vs1=c(0,0,0,1,1,1))
options(digit=3)

# Ordinary fit
fit <- lmFit(y,design)
fit <- eBayes(fit)
fit
as.data.frame(fit[1:10,2])

etc
... that is lmfit .. the engine of limma, just takes a matrix... just
ensure to start with log2... you are probably good to go ,just make up a
design matrix

-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Markus Seto
Sent: Thursday, June 26, 2008 5:39 PM
To: Mark Robinson
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] using the Limma package with matrix data

Hi Mark,

Thanks for the reply.  I did look at the documentation, however, I
couldn't
find an example where the user simply has a matrix of normalized
expression
values, and fits the linear model to it - most examples use some kind of
loading function with accessory image information, such as
read.maimages().   I don't actually have this information because I'm
trying
to fit linear models to transformed data from discrete counts from EST
experiments ... sorry for the troubles.

Markus

On Wed, Jun 25, 2008 at 10:53 PM, Mark Robinson <mrobinson at wehi.edu.au>
wrote:

> Markus.
>
> I encourage you to read the limma documentation.  'lmFit', which is
the
> command you'll use to fit the linear model, will take several class
types as
> input, including a matrix.
>
> In fact, if you look at:
>
> ?lmFit
>
> ... you'll see that some of the code examples at the bottom operate on
a
> matrix of randomly generated numbers for illustration.
>
> Hope that helps.
> Mark
>
>
>
>
>
> On 26/06/2008, at 12:38 PM, Markus Seto wrote:
>
>   Hi,
>>
>> I'm new to R, and i want to use the R software package Limma to
compute
>> some
>> tests for differential gene expression.  However, my data is in a
>> matrix-style format (with the Huber variance stabilization transform
>> applied), where columns are samples, and rows are genes - what would
the
>> easiest way to use this package be?  The R interface seems to only
work
>> well
>> when the data is available in GPR format?
>>
>> Thanks for any help you can provide,
>>
>> Markus
>>
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>>
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>

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