南京大学学报（自然科学版）2024，Vol.60Issue(6)：920-929,10.DOI:10.13232/j.cnki.jnju.2024.06.004
基于分类自动编码器的单细胞RNA测序数据降维方法scAC
A dimensionality reduction algorithm scAC for single-cell RNA-seq data based on categorical autoencoders
摘要
Abstract
Single-cell RNA sequencing(scRNA-seq)technology enables researchers to measure gene expression across the transcriptome at single-cell resolution,progressively transforming our understanding of cell biology and human diseases.However,the high variability,sparsity,and dimensionality of single-cell sequencing data have significantly impeded downstream analysis,making dimensionality reduction crucial for the visualization and the subsequent analysis of high-dimensional scRNA-seq data.Yet,existing single-cell dimensionality reduction algorithms have not adequately considered relationships intercellular,nor have jointly optimized the tasks of dimensionality reduction and clustering.To overcome these limitations,this study focuses on scRNA-seq data and employs machine learning techniques to investigate a dimensionality reduction algorithm based on autoencoders.In light of the fact that most existing dimensionality reduction algorithms do not consider the use of pseudo-labels to supervise the training process of the encoder,leading to the loss of intercellular signals during the dimensionality reduction of data,this paper proposes a cell dimensionality reduction algorithm based on the classified autoencoder.The algorithm combines the classified autoencoder with deep embedded clustering to generate a low-dimensional representation of the gene expression matrix.Experimental results demonstrate that compared to six other benchmark testing algorithms,this algorithm exhibits competitive performance in a range of downstream scRNA-seq analysis tasks.关键词
分类自动编码器/细胞降维/深度嵌入聚类/单细胞RNA测序/机器学习Key words
classification autoencoder/cell dimension reduction/deep embedding clustering/scRNA-seq/machine learning分类
信息技术与安全科学引用本文复制引用
唐勇轩,梁潇,骆嘉伟..基于分类自动编码器的单细胞RNA测序数据降维方法scAC[J].南京大学学报（自然科学版）,2024,60(6):920-929,10.基金项目
国家自然科学基金(62032007,62372165) （62032007,62372165）
