On Thu, Nov 1, 2012 at 6:17 AM, priya [guest] <guest@bioconductor.org>wrote:

>
> I have a dataset in the following way :
>
> ID_REF GSM362180    GSM362181  GSM362188    GSM362189  GSM362192
> 244901 5.094871713 4.626623079 4.554272515 4.748604391 4.759221647
> 244902 5.194528083 4.985930299 4.817426064 5.151654407 4.838741605
> 244903 5.412329253 5.352970877 5.06250609  5.305709079 8.365082403
> 244904 5.529220594 5.28134657  5.467445095 5.62968933  5.458388909
> 244905 5.024052699 4.714631878 4.792865831 4.843975286 4.657188246
> 244906 5.786557533 5.242403911 5.060605782 5.458148567 5.890061836
>
> I would like to cluster it column-wise to find out the similar promoters(
> columns represent the promoters and the rows - the genes). I used pv clust
> to do it earlier but would like to have a more detailed clustering ( maybe
> with hierarchial type ?) but am not sure how to do it.
>
>
It sounds like you want to do unsupervised analysis of your data.  There
are MANY ways to do this including using MDS plots, PCA, NMF, various forms
of hierarchical clustering, and many others.

See the help for hclust if you just want to try hierarchical clustering.  I
would suggest using a heatmap, though, as a starting place.  See the help
for heatmap.2 in the gplots package, for example.  Either way, you'll need
to reduce the number of genes that go into the heatmap; a typical approach
to doing so is to use a variance filter and then choose the top N most
variable (informative) genes.

Sean



>  -- output of sessionInfo():
>
> R version 2.15
> LINUX OPERATING SYSYTEM
>
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