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...ressed genes compare A to B situation. I got this data(this is a part of data) from whole organism , and each number means each expression values (that means, we could think 'a' gene is 13 in A situation, and it turns 30 in B situation) To findout probability, I'm going to use Audic - Claverie Method. ( The significance of digital gene expression profiles. 1997) But using this equation p(x|y), I have to calculate (x+y)! first. but I can't calculate (157+221)! or (666+1387)! in R. That's probabily the handling problem of huge number, How could I calculate p value in this data...

Hi, On Fri, Jun 4, 2010 at 5:18 AM, Pekka Nikander <pekka.nikander at nomadiclab.com> wrote: >  Would the best way be to add an option to -loop-unroll, and hack away at lib/Transforms/Utils/LoopUnroll.cpp? Instead, the better alternative is to write another pass similar to LoopUnrollPass.cpp (say LoopPeelPass.cpp) and add new option -loop-peel. The new pass could use llvm::UnrollLoop()

I am working on heavily optimising unusually static C++ code, and have encountered a situation where I basically want an optimiser that would speculatively unroll a loop to see if the first round of the loop could be optimised further. (I happen to know that it is possible.) The previous optimisations that produce the loop in the first place already do a magical job (relying heavily on constant

Hi all, I currently have four drives in my OpenSolaris box. The drives are split into two mirrors, one mirror containing my rpool (disks 1 & 2) and one containing other data (disks 2 & 3). I''m running out of space on my data mirror and am thinking of upgrading it to two 2TB disks. I then considered replacing disk 2 with a 2TB disk and making a RAIDz from the three new drives.