A clustering recommendation method for single-cell transcriptome sequencing data based on two-dimensional distribution structure determination

Abstract

The invention discloses a single-cell transcriptome sequencing data clustering recommendation method based on two-dimensional distribution structure determination, comprising: obtaining a gene expression matrix obtained from single-cell transcriptome sequencing data of multiple cells, after filtering and standardization, Construct a two-dimensional feature matrix and perform linear normalization; calculate the Euclidean distance between cells according to the normalized two-dimensional feature matrix, so as to establish the minimum spanning tree of cells; cut the minimum spanning tree of cells by adaptive threshold, and use The balance of the clusters formed after cutting is used to determine the two-dimensional distribution structure of the data; for data with fuzzy inter-cluster boundaries and continuous two-dimensional distribution structure, a hierarchical clustering algorithm is recommended and applied, while for data with obvious inter-cluster boundaries and block For data with two-dimensional distribution structure, recommend and apply spectral clustering algorithm. The present invention can recommend a method that is more suitable for the two-dimensional distribution structure of single-cell transcriptome sequencing data in hierarchical clustering and spectral clustering as a downstream clustering analysis method, thereby improving clustering accuracy.

Description

technical field [0001] The invention relates to the field of bioinformatics, and relates to a single-cell transcriptome sequencing data clustering recommendation method based on two-dimensional distribution structure determination. Background technique [0002] In the field of cell biology, single-cell analysis is the study of genomics, transcriptomics, proteomics, and metabolomics at the single-cell level. It provides an ultrasensitive tool to elucidate specific molecular mechanisms and pathways and reveal the nature of cellular heterogeneity. With the development of technology and the decline of cost, transcriptome sequencing (scRNA-seq) technology applied to single-cell whole genome is rapidly becoming the choice in many fields such as biology and biomedical research. Studying genome-wide gene expression at single-cell resolution overcomes the inherent limitations of traditional RNA-sequencing, and single-cell transcriptome sequencing enables researchers to more rigorous...

AI Technical Summary

Problems solved by technology

Therefore, spectral clustering can deal with more complex data distribution structures, such as fuzzy boundary problems, but the disadvantage of the method is that it relies heavily on the accuracy of the similarity matrix

[0005] Because the two clustering methods are based on different theories and strategies, there may be differences in the clustering results on data with different distribution structures

Images