trevismd / permutations-stats Goto Github PK

Hi, I have just started to use your permutation test package. I am not a programmer, so it took a bit of time to figure out how to download and install on my system, where it is not standardly present. I am using the Digital Research Alliance of Canada system, used to be called Compute Canada. I have a couple of questions:

First, I was able to install the wheel version, but for some reason not the tar.gz version, my system had Path troubles it seems. Anyway, I can import the program in Python and run the test. Question 1, where is the package actually? I do not see any obvious new directories or files in my home directory, I wouldn't think it could be installed more generally. Does the installation update some path variables in my home account so python knows where to look for permutations_stats.permutations ?

More importantly, is there some general documentation on setting some parameters in the system, like the number of permutations, whether to permute both X and Y (in my case I am doing a Brunner Munzel test with cases and controls, so just two data variables)? I was able to run the test with default parameters when X and Y both had around a dozen values, but when I tried to use my real data, which has over 100 values for one of the two, the system "killed" the program so presumably I exceeded memory allocation. I can request more memory, but need to have some rough idea of how much memory is required for a certain large number of data points. Is it possible to permute only one of the two data variables? Is it meaningful to do that?

Some of this info can be found on the web for other permutation test packages (like for R), but your implementation may be different so it's best to ask at the source.

You don't seem to have a literature citation posted here, is there one now that I can cite when I write this up? Otherwise do I just mention it as software used in my Methods section with a link to here?

FYI, this is for a human genetics project. We sequenced a bunch of cases and controls to look for mutations causing a rare endocrine phenotype, focused on very rare genomic variants. As a result we are doing a kind of gene burden test, and want to compare the burden of variants in the cases vs controls. For particular reasons, a Fisher or chi-square 2x2 test is not appropriate here, rather we are using Mann-Whitney or Brunner-Munzel, essentially to compare the average per sample mutation burden by ranking the samples. Because the number of cases is small (12), I wanted to run a permutation version just in case the regular tests (BM or MW) are too forgiving. BM is probably better generally as we don't know whether the variances are the same in the cases and controls, although they likely are.

Thanks and cheers from Montreal!
Mark Samuels
Associate Professor in Medicine
University of Montreal