"Steve Moore" <[EMAIL PROTECTED]> writes: > I have a question that I was hoping someone may be able to shed some > light on. If I wanted to analyse 1000-2000 arrays in R, how much > computing power would I need in order for the program to run O.K. Any > help that anybody could give me would be greatly appreciated.
Are the arrays that you speak of microarrays, such as manufactured by Affymetrix? If so, it may be a good idea also to send your question to the bioconductor mailing list <[EMAIL PROTECTED]>. I expect that any answers to your question will require you to be more specific about how you plan to analyze your data. Because R works "in memory" the primary bottleneck in using R on large datasets is the amount of memory that you have available on the computer. A typical Linux or Windows workstation or server can address up to 4GB of memory and you could expect to buy computers with 3GB or 4GB memory for a small fraction of what 1000-2000 Affymetrix chips and the preparation of your samples will cost. More than 4GB will require switching to processors other than Pentium and Athlon. One interesting possibility is the newly introduced AMD Opteron which is a 64-bit processor that uses an extension of the x86 instruction set. This is not to say that memory will be the only issue. As I said above, it will be necessary to have some idea of what you plan to do before meaningful advice can be offered. ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
