Method of identifying cell types based on single-cell RNA sequencing data

Abstract

The invention provides a method of identifying cell types based on single-cell RNA sequencing data. According to the method, a low-rank representation model of a high-dimensional matrix is effectivelycombined with a graph regularization theory; an optimization model is constructed by considering the global structure and local structure characteristics of data, the model is solved by adopting an alternating direction multiplier method (ADMM) to obtain a reliable inter-cell similarity matrix, and then the similarity matrix is clustered by adopting a spectral clustering method, so that single cells are clustered, and the cell types are identified. According to the method, the clustering effect of the single-cell RNA sequencing data can be remarkably improved.

Description

technical field [0001] The invention relates to the cross-research field of mathematics and biology, in particular to a method for cell classification through a clustering algorithm. Background technique [0002] What traditional sequencing technology obtains is the average of the gene expression values ​​of a group of cells (a mixture of tumor cells, immune cells, fibroblasts and macrophages), which ignores the differences in gene expression between cells, and it is difficult to distinguish between cells. expression heterogeneity. [0003] In recent years, with the continuous development of biotechnology, single-cell RNA sequencing technology can obtain the expression information of a large number of genes in a single cell. Heterogeneity provides a very powerful tool. Compared with traditional whole-genome sequencing, single-cell sequencing not only measures gene expression levels more accurately, but also detects trace gene expressors or rare non-coding RNAs. Its advanta...

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Problems solved by technology

[0006] Although the above methods have done some work on the clustering of single-cell RNA sequencing data, the accuracy and efficiency of clustering single-cell RNA sequencing data still need to be improved.

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