Parallel Clustering of Single Cell Transcriptomic Data with Split-Merge Sampling on Dirichlet Process Mixtures

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Tiehang Duan, José P. Pinto, Xiaohui Xie
Journal/Conference Name Bioinformatics
Paper Category
Paper Abstract Motivation With the development of droplet based systems, massive single cell transcriptome data has become available, which enables analysis of cellular and molecular processes at single cell resolution and is instrumental to understanding many biological processes. While state-of-the-art clustering methods have been applied to the data, they face challenges in the following aspects (1) the clustering quality still needs to be improved; (2) most models need prior knowledge on number of clusters, which is not always available; (3) there is a demand for faster computational speed. Results We propose to tackle these challenges with Parallel Split Merge Sampling on Dirichlet Process Mixture Model (the Para-DPMM model). Unlike classic DPMM methods that perform sampling on each single data point, the split merge mechanism samples on the cluster level, which significantly improves convergence and optimality of the result. The model is highly parallelized and can utilize the computing power of high performance computing (HPC) clusters, enabling massive clustering on huge datasets. Experiment results show the model outperforms current widely used models in both clustering quality and computational speed. Availability Source code is publicly available on https//github.com/tiehangd/Para_DPMM/tree/master/Para_DPMM_package
Date of publication 2018
Code Programming Language C++
Comment

Copyright Researcher 2022