20111212 RAT

These are tools and scripts to run various analysis workflows using sparse linear regression.
This is a second version compared to SLRplus, which was avery general set of tools.
SLR2 is far more specific for workflows that I have tested and some I have found valuable. These workflows center around creating netwroks of coupled oscillators where the couplings (or network edges) are defined as the non zero regression coefficents; however, these tools can be used for any elastic net regression.

Lots of stuff going on in here. Tools to estimate p-values, improtance scores tools for choosing parameters, calculating validation metrics, estimating errors and so on. Many of the methods are typcially time intensive, some simplifications are avalible, but the accuracy/differences are not tested (I think most of the simplificaitons give poor results) The most valuable found for identifying network structure (not as much the actual values) can be found in SLR2_3.py called run_1, the output is set up so that jobs can be distributed for many regressions and the results aggrigated into a single set of files.

I used python 2.7, numpy 1.6.1, and scipy 0.10.0.

IMPORTANT You need the wrapper and the original fortran code for glmnet to do anything useful. The code is maintained at: https://github.com/dwf/glmnet-python. There is a little trick in the compile step so read the readme for that module.

To improve efficiency of p-value calculations I used an analysis (and code) from: http://informatics.systemsbiology.net/EPEPT/. The original code was in matlab, and I converted it to python around 20111205. (https://github.com/Rtasseff/gpdPerm)

To allow for multiprocessing of jobs I used code (dispatcher/) from a colleague (JR) which may be included, this only impacts the files runSLR.py and runMP.py and not the above functionality. (https://github.com/Rtasseff/dispatch)

There is a license for the wrapper and the original fortran code but it is not my intention to distribute or maintain that code so I will leave you to find it when and if you decide to download it (note: it is freely available).

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