A dimensionality reduction algorithm scAC for single-cell RNA-seq data based on categorical autoencoders
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.
国家科技期刊平台
NSTL
万方数据
