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

A technology for sequencing data and single cells, applied in the cross-research field of mathematics and biology, can solve problems such as the need to improve accuracy and efficiency, and achieve the effect of improving the clustering effect.

Active Publication Date: 2020-02-14
EAST CHINA JIAOTONG UNIVERSITY
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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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  • Method of identifying cell types based on single-cell RNA sequencing data
  • Method of identifying cell types based on single-cell RNA sequencing data
  • Method of identifying cell types based on single-cell RNA sequencing data

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[0056] The beneficial effects of the present invention will be described in detail below in conjunction with examples, aiming to help readers better understand the essence of the present invention, but not to limit the implementation and protection scope of the present invention.

[0057] The present invention provides a method for identifying cell types based on single-cell RNA sequencing data, which is based on a matrix low-rank representation model and a method of graph regularization constraints to cluster noisy high-dimensional sparse single-cell RNA sequencing data for effective mining Global structural features and local association properties from single-cell RNA-sequencing data, leading to new computational methods for predicting key proteins. The main steps of the method include:

[0058] (1) Based on the single-cell RNA sequencing data X, the construction of the similarity matrix between cells is transformed into an optimization problem, and the mathematical model o...

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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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B40/20G16B40/30
CPCG16B40/20G16B40/30
Inventor 张伟徐佳李圆媛陈海林薛晓丽
Owner EAST CHINA JIAOTONG UNIVERSITY
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