Single-cell transcriptome sequencing data clustering recommendation method based on two-dimensional distribution structure judgment

Abstract

The invention discloses a single-cell transcriptome sequencing data clustering recommendation method based on two-dimensional distribution structure judgment, which comprises the following steps of: acquiring a gene expression matrix obtained by single-cell transcriptome sequencing data of a plurality of cells, and after filtering and standardization processing, constructing a two-dimensional feature matrix and carrying out linear normalization; calculating an Euclidean distance between cells according to the normalized two-dimensional feature matrix, thereby establishing a cell minimum spanning tree; cutting the cell minimum spanning tree through a self-adaptive threshold, and determining a two-dimensional distribution structure of data according to the balance of clusters formed after cutting; and recommending and applying a hierarchical clustering algorithm for data with fuzzy inter-cluster boundaries and a continuous two-dimensional distribution structure, and recommending and applying a spectral clustering algorithm for data with obvious inter-cluster boundaries and a block two-dimensional distribution structure. According to the method, a method which is more suitable for a two-dimensional distribution structure of single cell transcriptome sequencing data in hierarchical clustering and spectral clustering can be recommended to serve as a downstream clustering analysis method, and the clustering accuracy is improved.

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