Cluster Analysis Of Single-cell Sequencing Data Based On Transposon Element Expression

Single cell sequencing,as a technology that can obtain gene expression data at the cellular level,has broad application prospects in revealing cell types and functions,understanding cell development and disease processes,and other aspects.At the same time,transposable elements,as movable DNA fragments,have played an important role in the composition,function and evolution of the human genome.Single-cell transcriptome sequencing(sc RNA-seq)and single-cell chromatin accessibility sequencing(sc ATAC-seq)are currently the two most popular methods in single-cell sequencing technology.These two techniques provide information on the transcriptome and chromatin accessibility of cells,respectively.In this context,joint clustering analysis has become a powerful tool to combine the information from both datasets and accurately determine cell types and states.In response to the above issue,this article integrates gene clustering information from single-cell ATAC sequencing and single-cell RNA sequencing data in two scenarios and combines the matrix of transposable elements.By adding the combined transposable element expression,the gene clustering results are further clustered to improve the clustering results.The main work of the paper is as follows:(1)The quantitative method of single cell ATAC sequencing transposable elements based on open chromatin proposed in this paper uses single cell ATAC sequencing data to locate and annotate transposable elements in the genome.Determine the presence or absence of each transposable element in a single cell from ATAC sequencing data.By combining the comparison results with the transposable element index,obtain the transposable element expression matrix,and achieve quantitative analysis of transposable element expression in single-cell ATAC sequencing data.This method demonstrated good quantitative accuracy and consistency in the expression profile of transposable elements when using a human embryonic stem cell dataset as an example.The proposal of this method has certain significance for revealing the characteristics and functions of the expression of transposon elements in single-cell ATAC sequencing data,as well as for the study of repetitive sequences in the genome.(2)A enhanced clustering method based on transposable element expression was proposed for the joint analysis of single cell RNA sequencing and single cell ATAC sequencing data.This method first utilizes the quantitative method of single cell ATAC sequencing transposable element expression proposed in this article to obtain the transposable element expression matrix in single cell ATAC sequencing data.Then,the matrix is combined with the gene expression matrix in single cell RNA sequencing data for joint analysis.By using graph clustering method,transposable element expression is added as a new feature to the cell clustering process,thereby improving the accuracy and stability of cell clustering.This method not only improves the comprehensive analysis performance of single-cell RNA sequencing data and single-cell ATAC sequencing data,but also has certain help in exploring the role of transposable elements in cell function and evolution.Toolkit access link: https://github.com/hbBigShuaiB/TECT...