A flexible and fast mixed model toolbox.
Project description
TODO for 1.1.0
[ ] limix.qtl.qtl_test_lmm: 10k by 500k datasets, low memory footprint (i.e., <=60 GB).
[ ] limix.qtl.qtl_test_glmm: 10k by 500k datasets, low memory footprint (i.e., <=60 GB).
[ ] Both limix.qtl.qtl_test_glmm and limix.qtl.qtl_test_lmm should accept and take advantage of dask array.
[ ] limix.qtl.qtl_test_lmm: show progress bar like limix.qtl.qtl_test_glmm.
[ ] limix.qtl.qtl_test_lmm, limix.qtl.qtl_test_glmm: raise exception for wrong parameters (NaN, Inf) and coerce sequences to numpy arrays.
[ ] Standardize arguments, set labels, set title, set colors, for plotting functions: limix.plot.plot_manhattan, limix.plot.qqplot, limix.plot.plot_kinship.
[ ] Define ConvergenceError exception to be raised whenever our methods doesn’t converge.
[ ] Convert documentation style to numpy style, of every function.
TODO for 1.2.0
[ ] Nicely print a summary of a QTL and heritability result.
[ ] I/O for BGEN file format.
[ ] Add full-world QTL and heritability analysis into readthedocs.
Limix
Genomic analyses require flexible models that can be adapted to the needs of the user. Limix is a flexible and efficient linear mixed model library with interfaces to Python.
Limix includes methods for - single-variant association and interaction testing, - variance decompostion analysis with linear mixed models, - association and interaction set tests, - as well as different utils for statistical analysis, basic i/o and plotting.
A description of the public interface is found at https://limix.readthedocs.io/.
iPython notebook tutorials are available from github repository: https://github.com/limix/limix-tutorials.
These tutorials can also be viewed using the ipython notebook viewer: http://nbviewer.ipython.org/github/limix/limix-tutorials/blob/master/index.ipynb.
Highlights
iSet: an interaction set test with external context (paper) (public interface) (tutorial)
mtSet: an efficient multi-trait set test (paper) (public interface) (tutorial)
Install
The recommended way of installing it is via pip
pip install limixProblems
If you encounter any issue, please, submit it.
License
This project is licensed under the Apache License (Version 2.0, January 2004) - see the LICENSE file for details
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