Note

• The module is based on nimfa 1.3.2 now.
• The scripts to run on single-cell RNAseq data are put under the folder: sc-RNAseq_scripts
• The data used in the paper are in the folder: sc-RNAseq_data
• The input files (e.g., "lungEpithelium_full_input_.csv") should be put in the same folder with NMF_scRNAseq.py
• The NMF_scRNAseq.py could run iteratively on subgroups, controlled by max_depth
• The NMF_scRNAseq.py run in the desired rank ranges, specificed by --min_rank and --max_rank

install all necessary dependencies before hand

• python: pandas, docopt, nimfa
• R: pheatmap (ccshao/pheatmap), vegan, RColorBrewer

Example useages:

python NMF_scRNAseq.py -h  
python NMF_scRNAseq.py --min_rank=4 --max_rank=5 --max_depth=2 --RHeatmap=/fullpath/nmfHeatmap.R

---

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.

Important links

• Official source code repo: https://github.com/marinkaz/nimfa
• HTML documentation (stable release): http://nimfa.biolab.si
• 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://nimfa.biolab.si

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}
}