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

Project description

Topic Models for Single Cell Clustering

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:alt: Documentation Status

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

.. code-block:: console

$ pip install tmscc

for more information, see

.. 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(
# LDA's estimation (This takes some time.)
# 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:




* Python >= 3.5
* Java >= 1.8


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

.. _Mallet:


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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