Implementation for CS-LMM (Constrained Sparse multi-locus Linear Mixed Model)
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README.md
cslmm.py
setup.py

README.md

CS-LMM (Constrained Sparse multi-locus Linear Mixed Model)

Implementation of CS-LMM in this paper:

''Wang H, Aragam B, Lee S, Xing EP, and Wu W. Discovering Weaker Genetic Associations Guided by Known Associations, with Application to Alcoholism and Alzheimer's Disease Studies''

Introduction

CS-LMM is used to detect the weaker genetic association conditioned on the stronger validated associations.

File Structure:

  • models/ main method for CS-LMM
  • utility/ other helper files
  • cslmm.py main entry point of using CS-LMM to work with your own data

An Example Command:

python cslmm.py -n data/mice.plink

Data Support

  • CS-LMM currently supports CSV and binary PLINK files.
  • Extensions to other data format can be easily implemented through FileReader in utility/dataLoadear. Feel free to contact us for the support of other data format.

Installation

You will need to have numpy, scipy and pysnptool installed on your current system. You can install CS-LMM using pip by doing the following

   pip install git+https://github.com/HaohanWang/CS-LMM

You can also clone the repository and do a manual install.

   git clone https://github.com/HaohanWang/CS-LMM
   python setup.py install

Software with GUI

Software with GUI will be avaliable through GenAMap

Contact

Haohan Wang