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@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
>>
>>        [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor@stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives: http://news.gmane.org/
>> gmane.science.biology.informatics.conductor
>>
>
>

	[[alternative HTML version deleted]]

