Topic Models for Single Cell Clustering
A package for reducing dimension of gene expression profiles and doing clustering them.
Installation
$ pip install tmsccfor more information, see https://tmscc.readthedocs.io/en/latest/installation.html.
Example
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!
