scVSC: Deep Variational Subspace Clustering for Single-Cell Transcriptome Data

doi:10.1109/TCBB.2024.3405731

scVSC: Deep Variational Subspace Clustering for Single-Cell Transcriptome Data

2024-09-01

0

PRE

AI

DOI：10.1109/TCBB.2024.3405731

Original

Original request for help

Save

Abstract

En 中文

Single-cell RNA sequencing (scRNA-seq) is a potent advancement for analyzing gene expression at the individual cell level, allowing for the identification of cellular heterogeneity and subpopulations. However, it suffers from technical limitations that result in sparse and heterogeneous data. Here, we propose scVSC, an unsupervised clustering algorithm built on deep representation neural networks. The method incorporates the variational inference into the subspace model, which imposes regularization constraints on the latent space and further prevents overfitting. In a series of experiments across multiple datasets, scVSC outperforms existing state-of-the-art unsupervised and semi-supervised clustering tools regarding clustering accuracy and running efficiency. Moreover, the study indicates that scVSC could visually reveal the state of trajectory differentiation, accurately identify differentially expressed genes, and further discover biologically critical pathways.

Keywords:

scRNA-seq

clustering

deep subspace

variational inference

scVSC: Deep Variational Subspace Clustering for Single-Cell Transcriptome Data

Abstract

Journal

Organization