Single cell integrated clustering method based on subspace randomization

A clustering method, single-cell technology, applied in the field of data mining in bioinformatics
CN112735536AInactive Publication Date: 2021-04-30HUNAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUNAN UNIV
Publication Date
2021-04-30
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention relates to the field of data mining in bioinformatics, in particular to a single cell integrated clustering method based on subspace randomization. The method mainly comprises the following steps: (1) data preprocessing; (2) random subspace sampling is carried out to carry out cell similarity measurement; (3) subspace fusion is performed; and (4) single cell clustering is performed by measuring the overall similarity based on spectral clustering to obtain a final result. Compared with the prior art, the single cell clustering method provided by the invention is used for characterizing the novel cell type and detecting the heterogeneity in the population, and has stronger statistical ability and better stability. The method provided by the invention is feasible and effective, can achieve a good effect in the aspect of identifying the single cell cluster, and has important significance for researching cell type classification and identification of a complex data set.
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Description

technical field

[0001] The invention relates to the field of data mining in bioinformatics, in particular to a single-cell integrated clustering method based on subspace randomization. Background technique

[0002] As the basic structural and functional unit of organisms, single cells store important genetic information. During the process of cell proliferation and differentiation, many factors can lead to the occurrence of cell heterogeneity, such as cell state, cell microenvironment and regulation of intracellular processes. Previously, bulk sequencing technology typically analyzed tens of thousands of cells in total, where the gene expression value was the average score of all cells. As a result, it typically highlights population cell types while masking rarer cell types such as stem cells and cancer cells. Fortunately, single-cell RNA sequencing (scRNA-seq) technology can extract transcriptome information at single-cell resolution, changing the traditional transcripto...

Claims

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