Nimfa ----- [![build: passing](https://img.shields.io/travis/marinkaz/nimfa.svg)](https://travis-ci.org/marinkaz/nimfa) [![build: passing](https://coveralls.io/repos/marinkaz/nimfa/badge.svg)](https://coveralls.io/github/marinkaz/nimfa?branch=master) [![GitHub release](https://img.shields.io/github/release/marinkaz/nimfa.svg)](https://GitHub.com/marinkaz/nimfa/releases/) [![BSD license](https://img.shields.io/badge/License-BSD-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![Conda Version](https://img.shields.io/conda/v/conda-forge/nimfa.svg)](https://anaconda.org/conda-forge/nimfa) Nimfa is a Python module that implements many algorithms for nonnegative matrix factorization. Nimfa is distributed under the BSD license. The project was started in 2011 by Marinka Zitnik as a Google Summer of Code project, and since then many volunteers have contributed. See AUTHORS file for a complete list of contributors. It is currently maintained by a team of volunteers. [**[News:]**](https://github.com/marinkaz/scikit-fusion) [Scikit-fusion](https://github.com/marinkaz/scikit-fusion), collective latent factor models, matrix factorization for data fusion and learning over heterogeneous data. [**[News:]**](https://github.com/mims-harvard/fastGNMF) [fastGNMF](https://github.com/mims-harvard/fastGNMF), fast implementation of graph-regularized non-negative matrix factorization using [Facebook FAISS](https://github.com/facebookresearch/faiss). Important links --------------- - Official source code repo: https://github.com/marinkaz/nimfa - HTML documentation (stable release): http://ai.stanford.edu/~marinka/nimfa - Download releases: http://github.com/marinkaz/nimfa/releases - Issue tracker: http://github.com/marinkaz/nimfa/issues Dependencies ------------ Nimfa is tested to work under Python 2.7 and Python 3.4. The required dependencies to build the software are NumPy >= 1.7.0, SciPy >= 0.12.0. For running the examples Matplotlib >= 1.1.1 is required. Install ------- This package uses setuptools, which is a common way of installing python modules. To install in your home directory, use: python setup.py install --user To install for all users on Unix/Linux: sudo python setup.py install For more detailed installation instructions, see the web page http://ai.stanford.edu/~marinka/nimfa. Alternatively, you may also install this package using conda: conda install -c conda-forge nimfa Use --- Run alternating least squares nonnegative matrix factorization with projected gradients and Random Vcol initialization algorithm on medulloblastoma gene expression data: >>> import nimfa >>> V = nimfa.examples.medulloblastoma.read(normalize=True) >>> lsnmf = nimfa.Lsnmf(V, seed='random_vcol', rank=50, max_iter=100) >>> lsnmf_fit = lsnmf() >>> print('Rss: %5.4f' % lsnmf_fit.fit.rss()) Rss: 0.2668 >>> print('Evar: %5.4f' % lsnmf_fit.fit.evar()) Evar: 0.9997 >>> print('K-L divergence: %5.4f' % lsnmf_fit.distance(metric='kl')) K-L divergence: 38.8744 >>> print('Sparseness, W: %5.4f, H: %5.4f' % lsnmf_fit.fit.sparseness()) Sparseness, W: 0.7297, H: 0.8796 Cite ---- @article{Zitnik2012, title = {Nimfa: A Python Library for Nonnegative Matrix Factorization}, author = {Zitnik, Marinka and Zupan, Blaz}, journal = {Journal of Machine Learning Research}, volume = {13}, pages = {849-853}, year = {2012} } Selected publications (Methods) ------------------------------ - Data fusion by matrix factorization: http://dx.doi.org/10.1109/TPAMI.2014.2343973 - Jumping across biomedical contexts using compressive data fusion: https://academic.oup.com/bioinformatics/article/32/12/i90/2240593 - Survival regression by data fusion: http://www.tandfonline.com/doi/abs/10.1080/21628130.2015.1016702 - Gene network inference by fusing data from diverse distributions: https://academic.oup.com/bioinformatics/article/31/12/i230/216398 - Fast optimization of non-negative matrix tri-factorization: https://doi.org/10.1371/journal.pone.0217994 Selected publications (Applications) ------------------------------------ - A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family: https://www.nature.com/articles/s41467-017-01642-w - Gene prioritization by compressive data fusion and chaining: http://dx.doi.org/10.1371/journal.pcbi.1004552 - Discovering disease-disease associations by fusing systems-level molecular data: http://www.nature.com/srep/2013/131115/srep03202/full/srep03202.html - Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold: http://www.worldscientific.com/doi/pdf/10.1142/9789814583220_0038 - Matrix factorization-based data fusion for drug-induced liver injury prediction: http://www.tandfonline.com/doi/abs/10.4161/sysb.29072 - Collective pairwise classification for multi-way analysis of disease and drug data: https://doi.org/10.1142/9789814749411_0008 Tutorials --------- - Hidden Genes: Understanding cancer data with matrix factorization, ACM XRDS: Crossroads: https://dl.acm.org/citation.cfm?id=2809623.2788526 [[Jupyter Notebook]](https://nbviewer.jupyter.org/github/marinkaz/nimfa-ipynb/blob/master/ICGC%20and%20Nimfa.ipynb)