[BioC] Top 10% of genes based on p-value in TopTable

[Ovokeraye Achinike-Oduaran]

Thanks a bunch, Dan.

Regards,

Avoks.



On 08 May 2012, at 1:32 PM, Dan Du <tooyoung at gmail.com> wrote:

> hi Avoks,
> 
> well, that sounds like a totally different plot.
> To create such a cutoff benchmarking graph you have to make a new data
> structure holding your possible p threshold and the corresponding no. of
> significant genes. And this doesnot need ggplot and its advance syntax.
> 
> x<-rnorm(100)^2/10
> cut<-c(0.5,0.1, 0.05,0.01,0.005,0.001,0.0005,0.0001)
> plot(cut, sapply(cut, function(y) sum(x<=y)), ylab='No.SIG.DE.Gene',
> main='Title')
> 
> HTH,
> Dan
> 
> On Mon, 2012-05-07 at 14:55 +0200, Ovokeraye Achinike-Oduaran wrote:
>> Thanks Dan. One more question: I want to plot the no. of genes vs
>> p-value, so what goes into the aes portion of the command for the
>> non-p.value axis?
>> 
>> 
>> Thanks again.
>> 
>> -Avoks
>> 
>> On Mon, May 7, 2012 at 2:38 PM, Dan Du <tooyoung at gmail.com> wrote:
>>> Hi Ovokeraye,
>>> 
>>> if your current approach works fine, just change the first line would
>>> do,
>>> 
>>> results$threshold = as.factor(results$P.Value<=quantile(results$P.Value,
>>> 0.1))
>>> 
>>> and btw, limma does provide a function volcanoplot to do exactly the
>>> same thing.
>>> 
>>> HTH
>>> Dan
>>> 
>>> On Mon, 2012-05-07 at 14:15 +0200, Ovokeraye Achinike-Oduaran wrote:
>>>> Hi all,
>>>> 
>>>> I'm curious to know how I can get and highlight the top 10% of the
>>>> genes based on p-values that I get from my limma analysis in a volcano
>>>> plot.
>>>> 
>>>> I can get the genes highlighted based on an absolute logFC >2 and a
>>>> p-value<0.01(code below) but I would like to have an idea of the
>>>> number of genes in the top 10% based simply on p-values.
>>>> 
>>>> Any help will be greatly appreciated.
>>>> 
>>>> Thanks.
>>>> 
>>>> -Avoks
>>>> 
>>>> results$threshold = as.factor(abs(results$logFC) > 2 & results$P.Value < 0.01)
>>>> windows()
>>>> pdf("VolcanoPlot_GSE25724_9.pdf");
>>>> 
>>>> g = ggplot(data=results, aes(x=logFC, y=-log10(P.Value), colour=threshold)) +
>>>>  geom_point(alpha=0.4, size=1.75) +
>>>>  opts(legend.position = "none") +
>>>>  xlim(c(-8, 8)) + ylim(c(0, 10)) +
>>>>  xlab("log2 fold change") + ylab("-log10 p-value")
>>>> g
>>>> dev.off()
>>>> 
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>>> 
> 
>