[BioC] using the Limma package with matrix data

[Mark Cowley]

Hi Paul,

# if your data is in an object called yourData.... then
is.matrix( yourData )
[1] TRUE
# if the previous command is TRUE, then fit your linear model using:

model <- ......................
lmFit(yourData, model)

cheers,

Mark
-----------------------------------------------------
Mark Cowley, BSc (Bioinformatics)(Hons)

Peter Wills Bioinformatics Centre
Garvan Institute of Medical Research
-----------------------------------------------------

On 26/06/2008, at 5:57 PM, Paul Leo wrote:

>
>
> 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
>>>
>>>       [[alternative HTML version deleted]]
>>>
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>>
>>
>
> 	[[alternative HTML version deleted]]
>
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