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A package for reducing dimension of gene expression profilesand doing clustering them.

Project description

=======================================
Topic Models for Single Cell Clustering
=======================================


.. image:: https://img.shields.io/pypi/v/tmscc.svg
:target: https://pypi.python.org/pypi/tmscc

.. image:: https://img.shields.io/travis/tarohi24/tmscc.svg
:target: https://travis-ci.org/tarohi24/tmscc

.. image:: https://readthedocs.org/projects/tmscc/badge/?version=latest
:target: https://tmscc.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status




A package for reducing dimension of gene expression profiles and doing clustering them.

Installation
-------
.. code-block:: console

$ pip install tmscc

for more information, see https://tmscc.readthedocs.io/en/latest/installation.html.

Example
-------
.. code-block:: python

from tmscc import tm
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans

profile = pd.DataFrame(
np.arange(200).reshape([5, 40])
) # gene expression profile (genes*cells matrix)
profile.index = ['CHEK2', 'MSH2', 'PTEN', 'TSC1', 'HER2']

lda = tm.LDA(
n_topics=4,
profile=profile,
outdir='~/tmp',
)
# LDA's estimation (This takes some time.)
lda.estimate()
# lda's theta() can be used for clustering, such as k-means
kmeans = KMeans(n_clusters=2).fit_predict(lda.theta())


* Free software: MIT license
* Documentation: https://tmscc.readthedocs.io.


Features
--------

* TODO


Requirements
-------

* Python >= 3.5
* Java >= 1.8

Credits
-------

* This package owes what this is to `Mallet`_. Thank you for the wonderful toolkit!

.. _Mallet: http://mallet.cs.umass.edu/


=======
History
=======

0.2.0 (2018-03-16)
------------------

* Add LDA implementation.
* Add some documents.

0.1.0 (2018-03-14)
------------------

* First release on PyPI.

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