A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data

Overview

Clustering analysis is widely utilized in single-cell RNA-sequencing (scRNA-seq) data to discover cell heterogeneity and cell states. While several clustering methods have been developed for scRNA-seq analysis, the clustering results of these methods heavily rely on the number of clusters as prior information. How-ever, it is not easy to know the exact number of cell types, and experienced determination is not always accurate. Here, we have developed ADClust, an auto deep embedding clustering method for scRNA-seq data, which can simultaneously and accurately estimate the number of clusters and cluster cells. Specifically, ADClust first obtain low-dimensional representation through pre-trained autoencoder, and use the representations to cluster cells into micro-clusters. Then, the micro-clusters are compared in be-tween by Dip-test, a statistical test for unimodality, and similar micro-clusters are merged through a designed clustering loss func-tion. This process continues until convergence. By tested on elev-en real scRNA-seq datasets, ADClust outperformed existing meth-ods in terms of both clustering performance and the ability to es-timate the number of clusters. More importantly, our model pro-vides high speed and scalability on large datasets.

Requirements

Please ensure that all the libraries below are successfully installed:

• torch 1.7.1
• numpy 1.19.2
• scipy 1.7.3
• scanpy 1.8.1

Installation

You need to compile the dip.c file using a C compiler, and add the path of generated library dip.so into LD_LIBRARY_PATH. For this following commands need to be executed:

gcc -fPIC -shared -o dip.so dip.c

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./dip.so

Run ADClust

Run on the normalized example data.

python ADClust.py --name Baron_human_normalized

output

The clustering cell labels will be stored in the dir ourtput /dataname_pred.csv.

scRNA-seq Datasets

All datasets can be downloaded at Here

All datasets will be downloaded to: ADClust /data/

Citation

Please cite our paper:

@article{zengys,
  title={A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data},
  author={Yuansong Zeng, Zhuoyi Wei, Fengqi, Zhong,  Zixiang Pan, Yutong Lu, Yuedong Yang},
  journal={biorxiv},
  year={2021}
 publisher={Cold Spring Harbor Laboratory}
}