Cell Type Identification And Application Research Based On Single Cell Transcriptome Data

The rapid development of single-cell RNA sequencing technology has opened up a whole new perspective for identifying cell types in multicellular organisms and understanding the relationships between them.Distinguishing different cell types and subtypes allows the identification of different immune cells and components of different tumor clones in the tumor microenvironment,a fundamental task in tumor heterogeneity analysis that can help researchers understand the mechanisms of tumor immune escape and improve the understanding of diseases such as human tumors.Faced with sequencing data of thousands or even millions of cells generated from a single experiment,the current mainstream approach is to identify cell types by clustering a given single-cell transcriptome sequencing data.Although numerous scholars have proposed clustering algorithms for single-cell sequencing data,existing single-cell clustering algorithms regard both cell types and subtypes as cell populations with specific gene expression patterns,which is not conducive to accurate cell typing.Therefore,this paper proposes a cell similarity index that unifies the identification of cell types and subtypes(UCRSI).This approach assumes that genes representing cell type differences have an on/off expression pattern,while genes determining cell state display a gradient variation in expression as the state changes.The method separately calculates these two types of differences,then combines them with a consensus adjacency matrix,and finally completes cell typing using spectral clustering.Benchmark testing results using the Wilcoxon test show that UCRSI can more robustly reconstruct expert annotations of single-cell RNA sequencing datasets compared to existing methods.Furthermore,UCRSI can be used for tumor heterogeneity analysis and improving visualization of large numbers of cells.After completing cell clustering using UCRSI,this study employs the Cell Chat tool to analyze intercellular interactions,revealing the complex relationship between tumor cells and immune cells within the tumor microenvironment.We noticed that as the tumor progresses,the patterns of cell-cell interactions also show dynamic changes.To accurately capture these changes,we utilize graph embedding to reveal the characteristic differences of cell interactions in different stages and different tumor subclones.Specifically,we first transform the patient’s cell interaction network into a low-dimensional vector space using graph embedding technology,then construct a predictive model to predict lymph node metastasis.This method enables us to clearly demonstrate that the cell interaction networks of patients at different stages exhibit certain differences.Finally,using the Fisher exact test,we identify differences between different subtypes and stages,and based on these differences,we rebuild the predictive model to improve the accuracy of prediction.