hlmm is a python library for fitting heteroskedastic linear mixed models to genetic data.
A heteroskedastic linear model (HLM) can model the effect of a set of variables on the mean of a response (such as a continuous phenotype) and the effect of a (potentially different) set of variables of the variability of the response
A HLM models all effects on both the mean and variance of a response as fixed effects. A heteroskedastic linear mixed model (HLMM) adds modelling of random effects of a set of variables on the mean of the response.
Modelling random effects can make fitting a HLMM very computationally demanding, with the number of operations scaling with the cube of sample size.
The package hlmm provides the ability to fit a HLMM much more quickly when the number of variables with random effects is small compared to the sample size. We use an algorithm whose operations scale in proportion to the sample size multiplied by the number of random effects squared.
HLM class: given data, find the parameters that maximise the likelihood and their sampling distribution.
HLMM class: given data, find the parameters that maximise the likelihood and their sampling distribution.
hlmm_chr.py: command line script that fits heteroskedastic linear models or heteroskedastic linear mixed models to a contiguous segment of the genome. The script takes bed formatted genotypes as input and can incorporate covariates for the fixed effects on the mean and/or variance.
We recommend installing using pip (https://pip.pypa.io/en/stable/). At the command line, type
'sudo pip install hlmm'We recommend reading the documentation for this package (http://hlmm.readthedocs.io/en/master/) and working through the tutorial (http://hlmm.readthedocs.io/en/master/tutorial.html).
hlmm has the following dependencies:
python 2.7
Packages:
We highly recommend using a python distribution such as Anaconda (https://store.continuum.io/cshop/anaconda/). This will come with both numpy and scipy installed and can include an MKL-compiled distribution for optimal speed.
To install from source, clone the git repository, and in the directory containing the HLMM source code, at the shell type
'sudo python setupy.py install'or, on the windows command prompt, type
'python setup.py install' The tests directory contains scripts for testing the computation of the likelihoods, gradients, and maximum likelihood solutions for both heteroskedastic linear models (test_hetlm.py) and for heteroskedastic linear mixed models (test_hetlmm.py). To run these tests, a further dependency is required: numdifftools.
To run the tests, first install hlmm. Change to the tests/ directory and at the shell type
'python test_hetlm.py'
'python test_hetlmm.py'Both tests should run without any failures.