Cell type identification method based on similarity learning and enhancement thereof

An identification method and similarity technology, applied in the field of bioinformatics

Active Publication Date: 2019-09-10
CENT SOUTH UNIV
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Although there are some methods for cell type identification, there is ro

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  • Cell type identification method based on similarity learning and enhancement thereof
  • Cell type identification method based on similarity learning and enhancement thereof
  • Cell type identification method based on similarity learning and enhancement thereof

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Embodiment Construction

[0070] The present invention will be further described below in conjunction with the flow chart and specific embodiments.

[0071] The invention discloses a method for identifying cell types based on similarity learning and its enhancement. Aiming at the characteristics of high-level noise in single-cell data, the method uses a new global similarity calculation method that is different from the traditional calculation of local similarity of cells . And make full use of the advantages of different similarities, learn better similarity through gene selection strategy and similarity enhancement strategy, and finally generate more accurate cell type identification results based on the learned similarity.

[0072] like figure 1 As shown, a cell type identification method based on similarity learning and its enhancement, including the following steps:

[0073] Step 1: Gene filtering;

[0074] Delete genes whose expression values ​​are 0 from the given gene expression matrix of al...

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Abstract

The invention discloses a new cell type identification method based on similarity learning and enhancement thereof. The method designs a new global similarity calculation method, combines with other three conventional local similarity information, screens the genes, and carries out enhancement processing on the global similarity with sparse properties. According to the method, a global similaritycalculation method different from the traditional calculation of the local point-to-point similarity is used, the gene selection and the similarity enhancement are carried out by combining multiple different similarities including the global similarity and the local similarity, and a similarity matrix rich in information is obtained. According to the method, the influence of the factors, such as the technical noise, the biological noise, etc., carried by the single cell data can be effectively reduced, and the type of the single cell can be more accurately identified.

Description

technical field [0001] The invention belongs to the field of bioinformatics and relates to a cell type identification method based on similarity learning and its enhancement. Background technique [0002] The rapid development of single-cell technology enables biological research to be carried out at the single-cell level. The emergence of single-cell RNA-seq technology has made the analysis based on single-cell transcriptome sequencing data one of the hot research topics, including cell heterogeneity analysis, cell fate analysis, disease pathogenesis and so on. In this series of related research topics, cell type identification plays a fundamental but important role. However, unlike the previous cell population sequencing, which used the average expression value of a whole cell as the expression value of the cell population, single-cell sequencing only measures the expression level in a single cell. While this approach brings opportunities for related research, it also br...

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Application Information

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IPC IPC(8): G06K9/62G16B40/00
CPCG16B40/00G06F18/23213G06F18/24147G06F18/22
Inventor 李敏梁珍兰郑瑞清
Owner CENT SOUTH UNIV
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