[BioC] Comparison of murine and human microarray data

[Matthew McCall]

Ying,

This is getting close to the top of the to-do list. I'll probably get
to it in the next month. In the meantime, you can always make your own
vectors using the frmaTools package.

Best,
Matt



On Tue, Apr 22, 2014 at 2:07 PM, ying chen <ying_chen at live.com> wrote:
> Hi Matt,
>
> I just wonder is it possible for you to update the hgu133plus2frmavecs
> packages to use the most recent customCDF v18?
>
> Thanks a lot,
>
> Ying
>
>> Date: Tue, 22 Apr 2014 10:40:33 -0400
>> From: mccallm at gmail.com
>> To: sbrohee at ulb.ac.be
>> CC: basvgestel at hotmail.com; bioconductor at r-project.org
>> Subject: Re: [BioC] Comparison of murine and human microarray data
>
>>
>> Bas,
>>
>> My (biased) recommendation would be fRMA + barcode. Comparing human
>> and mouse microarray data is Figure 2 of the barcode paper:
>> http://nar.oxfordjournals.org/content/39/suppl_1/D1011.full
>>
>> You still need to be careful with the orthologous gene mapping.
>>
>> Best,
>> Matt
>>
>>
>> On Tue, Apr 22, 2014 at 10:22 AM, Sylvain Brohée <sbrohee at ulb.ac.be>
>> wrote:
>> > Hi Bas,
>> >
>> > First of all, I would say that I would not recommend to to this kind of
>> > stuff as it seems kind of dirty to me.
>> >
>> > However, recently, I was 'kindly' asked to perform this type of analyze
>> > and
>> > I used the good old ComBat function from the sva package to remove the
>> > batch
>> > effect between human and mouse. In order to have a reliable matrix from
>> > the
>> > beginning, I used only those genes that had the same gene names (in
>> > capital
>> > letters for mouse).
>> >
>> > This is the code I used :
>> >
>> > Let's eset be the mouse dataset and human.exp.names.agg be the human
>> > dataset. Genes are in rows and experiments in columns.
>> >
>> > row.names(eset) <- toupper(row.names(eset))
>> > human.mouse.complete <- merge(human.exp.names.agg, eset, by =
>> > 'row.names')
>> > row.names(human.mouse.complete) <- human.mouse.complete[,1]
>> > human.mouse.complete <- human.mouse.complete[,-1]
>> > pheno.mod0 <- data.frame(row.names = names(human.mouse.complete), fact.1
>> > =
>> > rep(1, ncol(human.mouse.complete)), fact.2 = rep(2,
>> > ncol(human.mouse.complete)))
>> > mod0 <- model.matrix(~1, data = pheno.mod0)
>> > human.mouse.complete.combat <- ComBat(human.mouse.complete, c(rep(1,
>> > human.exp.nb), c(rep(2,22))), mod = mod0)
>> >
>> > It seemed to give satisfactory results.
>> >
>> > If there are more "clever" ways, I would be happy to hear about them!
>> >
>> > Cheers,
>> >
>> > Sylvain
>> >
>> >
>> >
>> > On 04/22/2014 04:04 PM, Bas van Gestel wrote:
>> >>
>> >> Dear all,
>> >> For a project I would like to compare the gene expression of different
>> >> immune cells in both mouse and human. For the immune cells of interest,
>> >> microarray data is available. The microarray data for the human immune
>> >> cells
>> >> have been generated with the same platform. The microarray data for the
>> >> murine immune cells have been generated with the same platform,
>> >> although
>> >> with a different platform than used for the human immune cells. I
>> >> performed
>> >> RMA normalization using the rma function in the affy package separately
>> >> for
>> >> the human and the murine datasets. However, I would like to compare the
>> >> gene
>> >> expression levels of mouse and human immune cells. I therefore would
>> >> like to
>> >> ask you the following questions:What is the recommended way to
>> >> normalize the
>> >> RMA normalized datasets of human and mouse, so that I can
>> >> compare/combine
>> >> both datasets?
>> >> Thanks a lot for your help.
>> >> Kind regards, Bas
>> >> [[alternative HTML version deleted]]
>> >>
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>> >
>> >
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>>
>>
>> --
>> Matthew N McCall, PhD
>> 112 Arvine Heights
>> Rochester, NY 14611
>> Cell: 202-222-5880
>>
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-- 
Matthew N McCall, PhD
112 Arvine Heights
Rochester, NY 14611
Cell: 202-222-5880