[BioC] Visualizing GOstats HyperGTest results
James W. MacDonald
jmacdon at med.umich.edu
Tue Jan 22 15:15:04 CET 2008
Hi Srini,
Srinivas Iyyer wrote:
> Dear Group,
> Apologies for a similar re-post on this issue.
>
> I kindly want to bring to the attention of
> bioconductor developers, if there is a function to
> generate a heatmap of all enriched GO categories after
> a conditional hyperGTest.
>
> For example, I have 5 different time points
> time-series data and with 3 different doses. I run
> HyperGTest and I endup with atleast 50 categories for
> 5 time points and 3 drug treatments.
>
> 3hr @ d1; 3hr @ D2 ; 3 hr @ D3 - 50, 50 ,50 cats
> respectively.
>
> Like wise for 6 and 12 hrs for D1,D2 and D3.
>
> I have finally many HTML tables after testing for all
> time points and drugs.
>
> If I have a function to generate a heatmap for D1
> all 3 time points on X-axis and a union of enriched
> categories on Y-axis and color them according to FDR
> or P-values, it would be a nice visualization. If
> somehow I can plot all timepoints and all drug effects
> along with GO categories, that would be a beautiful
> depiction of results.
>
> my questions are:
>
> 1. Is there an inbuilt GOstats function like
> that.(goCluster has something similar, however it is
> not as easy to combine for a basic R skillset person
> like me).
>
> 2. If not, is it possible to combine all results and
> use standard heatmap function to obtain a figure of
> what I wanted and described above.
Sure. Just use the summary() function with the pvalue argument equal to
1. This will return all the GO terms. You can then select those terms
that are significant in any comparison and then create a matrix
containing the p-values for each term for each comparison. You would
probably want to do something like take the -log_10 of these values and
then you could feed that matrix to heatmap().
Best,
Jim
>
> I appreciate if any one can suggest or help me in this
> question.
>
> thank you.
>
> srini
>
>
>
>
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--
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
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