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Microbial data clustering method based on robust symmetric non-negative matrix factorization

A non-negative matrix decomposition and data clustering technology, applied in complex mathematical operations, instruments, calculations, etc., can solve problems such as high computational complexity, achieve the effects of enhancing local neighborhood, good performance, and avoiding the influence of noise

Pending Publication Date: 2021-11-30
ANYANG NORMAL UNIV
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  • Summary
  • Abstract
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Problems solved by technology

ClustRF builds a robust similarity network that can effectively capture the correlation between features, but the computational complexity is high, and it grows exponentially with the size of the sample and the number of features and training trees

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  • Microbial data clustering method based on robust symmetric non-negative matrix factorization
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  • Microbial data clustering method based on robust symmetric non-negative matrix factorization

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Embodiment Construction

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0038] The embodiment of the present invention discloses a microbial data clustering method based on robust symmetric non-negative matrix decomposition, such as figure 1 As shown, the specific steps include the following:

[0039] collect sample data;

[0040] Construct sample similarity matrix for sample data;

[0041] Use the similarity network fusion algorithm to integrate the sample similarity matrix to obtain the fusion matrix;

[0042] The fusion matr...

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Abstract

The invention discloses a microbial data clustering method based on robust symmetric non-negative matrix factorization, and relates to the technical field of microbial data clustering. The method specifically comprises the following steps: collecting sample data; constructing a sample similarity matrix for the sample data; integrating the sample similarity matrixes by using a similarity network fusion algorithm to obtain a fusion matrix; and clustering the fusion matrix through a robust symmetric non-negative matrix factorization algorithm, and distributing labels for the sample data. The invention provides a data integration framework RSNMF based on similar network fusion and symmetric non-negative matrix factorization, which is used for clustering microbiome data and effectively avoids the influence of noise.

Description

technical field [0001] The invention relates to the technical field of microbial data clustering, in particular to a microbial data clustering method based on robust symmetric non-negative matrix decomposition. Background technique [0002] In recent years, advances in high-throughput sequencing technology have made it possible to collect heterogeneous microbiome data at scale. Studying the interactions between microbes and between the microbiome and the host environment has shown increasing value in understanding the relationship between the microbiome and human disease. The initiation of many microbiome projects, including the Human Microbiome Project (HMP), Human Gut Metagenome Project (MetaHIT), etc., has accumulated a large amount of data. However, due to the large amount of noise and its heterogeneity in microbiome data, few methods integrate multi-source heterogeneous microbial data to study the composition and function of microbial communities. Therefore, there is ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F17/16
CPCG06F17/16G06F18/23G06F18/22G06F18/24147
Inventor 睢丹魏晨希刘芃兰
Owner ANYANG NORMAL UNIV