scBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means

Advances in single-cell RNA sequencing (scRNA-seq) technologies allow researchers to analyze the genome-wide transcription profile and to solve biological problems at the individual-cell resolution. However, existing clustering methods on scRNA-seq suffer from high dropout rate and curse of dimensionality in the data. Here, we propose a novel pipeline, scBKAP, the cornerstone of which is a single-cell bisecting K-means clustering method based on an autoencoder network and a dimensionality reduction model MPDR. Specially, scBKAP utilizes an autoencoder network to reconstruct gene expression values from scRNA-seq data to alleviate the dropout issue, and the MPDR model composed of the M3Drop feature selection algorithm and the PHATE dimensionality reduction algorithm to reduce the dimensions of reconstructed data. The dimensionality-reduced data are then fed into the bisecting K-means clustering algorithm to identify the clusters of cells. Comprehensive experiments demonstrate scBKAP's superior performance over nine state-of-the-art single-cell clustering methods on 21 public scRNA-seq datasets and simulated datasets. The source codes and datasets are available at https://github.com/YuBinLab-QUST/scBKAP/ and https://doi.org/10.24433/CO.4592131.v1.

Automated summary

This study proposes a novel clustering method called scBKAP, which addresses the issues of high dropout rate and curse of dimensionality in scRNA-seq data by utilizing an autoencoder network and a dimensionality reduction model MPDR. Comprehensive experiments on 21 public scRNA-seq datasets and simulated datasets demonstrate the superior performance of scBKAP over nine state-of-the-art single-cell clustering methods.