[BioC] Help on experiment design

[Marcelo Brandão]

Hi there
I am completely lost on how to map from probe to gene using nimblegen
data. Where can I find a tutorial or some kind of help. I see that I
need to generate a package, but I need my tabular file in a specific
order, does anyone knows what this order may be?

Cheers

Marcelo


Em 20 de agosto de 2010 00:50, Mark Cowley <m.cowley at garvan.org.au> escreveu:
> Hi Marcelo,
> take a look at the annotation packages here: http://bioconductor.org/packages/release/AnnotationData.html
>
> you'll need to do the mapping from probe to gene before running heatmap.2, since heatmap.2 won't do that conversion for you
>
> cheers,
> Mark
>
> On 20/08/2010, at 12:09 AM, Marcelo Brandão wrote:
>
>> Thanks Mark.
>>
>> Now, the data makes sense.
>> I draw the results on a heatmap (heatmap.2 from gplots), do you have
>> any idea on how to change the probes names to geneID? I am using
>> Nimblegen data and have an NDF file with all annotation.
>>
>> Cheers and thanks again
>>
>> Marcelo
>>
>> Em 19 de agosto de 2010 01:29, Mark Cowley <m.cowley at garvan.org.au> escreveu:
>>> hi Marcelo,
>>>
>>> If you use that design matrix, then you'll need to also fit contrasts
>>>> designEsp <- cbind(controle=c(1,1,1,0,0,0),experimento=c(0,0,0,1,1,1))
>>>> fitEsp <- lmFit(exprs(summarized),designEsp)
>>> contr.matrix <- c(-1,1)
>>> fitEsp <- contrasts.fit(fitEsp, contr.matrix)
>>>> fitEsp <- eBayes(fitEsp)
>>>> topTable(fitEsp,coef=1, adjust = "fdr", n = 10)
>>>
>>> or use this design
>>>> designEsp2 <- cbind(intercept=c(1,1,1,1,1,1),expvscon=c(0,0,0,1,1,1))
>>>> fitEsp2 <- lmFit(exprs(summarized),designEsp2)
>>>> fitEsp2 <- eBayes(fitEsp2)
>>>> topTable(fitEsp2,coef=2, adjust = "fdr", n = 10)
>>>
>>> cheers,
>>> Mark
>>> On 19/08/2010, at 10:59 AM, Marcelo Brandão wrote:
>>>
>>>> Hello all!
>>>> I am analyzing a set with 6 microarrays, 3 controls and 3
>>>> experimentals. I am interested in look after differentially expressed
>>>> genes. My doubt, among a lot of else, is if my experiment is designed
>>>> correctly. I am currently using the following comands:
>>>>
>>>> designEsp <- cbind(controle=c(1,1,1,0,0,0),experimento=c(0,0,0,1,1,1))
>>>> fitEsp <- lmFit(exprs(summarized),designEsp)
>>>> fitEsp <- eBayes(fitEsp)
>>>> topTable(fitEsp,coef=2, adjust = "fdr", n = 10)
>>>>
>>>> is it the best way to infer different expression? I am using nimblegen
>>>> microarrays.
>>>>
>>>> Thanks in advance.
>>>>
>>>> Marcelo
>>>>
>>>> --
>>>> Marcelo Mendes Brandão
>>>> Postdoc fellow
>>>> Laboratório de Biologia Molecular de Plantas - ESALQ/USP
>>>> Website: http://bioinfo.esalq.usp.br
>>>> AtPIN: http://bioinfo.esalq.usp.br/atpin
>>>> SKYPE: mmbrand
>>>> Tel: (+55) 19 3429 4442
>>>>
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>>>
>>>
>>
>>
>>
>> --
>> Marcelo Mendes Brandão
>> Postdoc fellow
>> Laboratório de Biologia Molecular de Plantas - ESALQ/USP
>> Website: http://bioinfo.esalq.usp.br
>> AtPIN: http://bioinfo.esalq.usp.br/atpin
>> SKYPE: mmbrand
>> Tel: (+55) 19 3429 4442
>
>



-- 
Marcelo Mendes Brandão
Postdoc fellow
Laboratório de Biologia Molecular de Plantas - ESALQ/USP
Website: http://bioinfo.esalq.usp.br
AtPIN: http://bioinfo.esalq.usp.br/atpin
SKYPE: mmbrand
Tel: (+55) 19 3429 4442