Metabolomics marker weight analysis universal method

Abstract

The invention provides a metabolomics marker weight analysis universal method which comprises the following steps of S1, performing metabolite correlation analysis; S2, performing network analysis fordetermining a key metabolism network; S3, performing multiple regression analysis and determining a metabolic marker; and S4, based on network degree centrality, determining importance of the metabolic marker in the key metabolism network. According to the method of the invention, by means of network analysis, importance of the metabolic marker in the key metabolism network is quantified throughnetwork degree centrality, thereby supplying an evaluation method for biological weight of the metabolic marker, improving accuracy in identifying the metabolic marker, and realizing high applicability in analyzing other metabolomics platform markers.

Description

technical field [0001] The invention relates to a general method for weight analysis of metabolomics markers, belonging to the technical field of metabolomics detection. Background technique [0002] With the continuous improvement of detection methods and data computing capabilities, omics technology based on high-throughput detection and big data analysis plays an increasingly important role in the research of multiple levels of biological genes, transcription, protein, and metabolism. . Omics technology measures and analyzes a large amount of biological information at the overall level, and expounds the potential mechanism of changes in the activities of living organisms under the influence of the environment or genes from a multi-dimensional and systematic perspective. [0003] As an important part of omics technology, metabolomics mainly studies small metabolic molecules below 1000 Da, and these small molecules can directly and accurately reflect the current physiologi...

AI Technical Summary

Problems solved by technology

However, the metabolism of organisms is a complex steady-state network system. A metabolite usually involves changes in multiple potential metabolic pathways. Although the analysis of the enrichment level of metabolic markers in known metabolic pathways in KEGG can explain some of the differences Due to the limitations of the database, the enrichment analysis results often ignore the influence of factors such as metabolic differences between species and unknown metabolic pathways.

At the same time, ignoring the correlation between metabolites and single database enrichment analysis will easily lead to bias in the evaluation of the biological effects of metabolic markers, thereby reducing the specificity of metabolic markers

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