Hi Tyler --
On 08/08/2011 12:54 PM, Tyler Gruhn wrote:
I just signed up for R Help, so please let me know if I'm doing this
incorrectly.
Better to provide an informative subject line -- "microarray heatmap
memory use" ?
I've been working with R for a couple of weeks, attempting to process
microarray data. This means 20,000+ rows of data to work with x 24
columns. I am trying to produce heatmaps and found that the best
computer I have available to me can process the data without any
clustering (Rowv=NA, Colv=NA), but my supervisor has made it clear that
I need to have the clusters present. I have been messing around with
the memory allocation commands, but I can't seem to get it to process
even still. Are there any methods of getting large amounts of data to
process into heatmaps that I should look into?
The Bioconductor project has many users experienced in analysis of
microarray data. Have you tried posting your question there?
http://bioconductor.org/help/mailing-list/
A common practice is to reduce the number of features (rows) by
filtering to exclude, e.g., those that are known to be control probes
and those with low across-sample variability. The genefilter package
contains functions for this.
http://bioconductor.org/packages/release/bioc/html/genefilter.html
It is important to include the output of
sessionInfo()
in your mailing list posts, so that the R and package versions you're
using are obvious. It also helps to create a small but realistic example
that others can execute, e.g. (although not very realistic)
m <- matrix(rnorm(25 * 20000), ncol=25)
heatmap(m)
Martin
Thank you very much,
Tyler
[[alternative HTML version deleted]]
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Computational Biology
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