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''
CS-LMM is used to detect the weaker genetic association conditioned on the stronger validated associations.
- 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
- CS-LMM currently supports CSV and binary PLINK files.
- Extensions to other data format can be easily implemented through
utility/dataLoadear. Feel free to contact us for the support of other data format.
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