Channel analysis modeling method based on mass spectrum metabonomics

Abstract

The invention provides a metabolic channel analysis modeling method based on mass spectrum metabonomics. The method comprises the following steps: S1, collecting biological samples of normal organisms and diseased organisms, converting mass spectra obtained by collecting the samples into mzML files, and carrying out centralization, denoising and alignment treatment on the mass spectrum files to obtain a two-dimensional data matrix of metabolites of the samples; s2, performing centralization and Unite Variance normalization processing on the two-dimensional data matrix of the metabolites of the samples and a sample category vector matrix; s3, acquiring a metabolite-channel mapping relation, and optimizing a channel weight coefficient; and 4, sorting the channels, adjusting a penalty factor, determining selected frequencies of the channels, and sorting the channels by using the selected frequencies of the channels. According to the method provided by the invention, the metabolites are grouped into mutually overlapped channels, a partial least square method and Group Lasso are combined to establish a regression model, and the channel weight coefficient and the penalty factor are introduced, so that multiple regression based on 'grouping sparse' is realized.

Description

Technical field [0001] The present invention relates to the field of data analysis, in particular to a path-analysis modeling method based on mass spectrometry metabolic. Background technique [0002] Modern high-profile mass spectrometry technology provides us with rich organism information, so that we can systematically study changes under the conditions of external stimulus, pathophysiology, and gene mutation, and gene mutation. Base spectrometry based metabolic syndrome as a modern analysis technology for comprehensive analysis of the body metabolism, in the field of academic research, especially in the field of biomedical and plant science. [0003] Data analysis is a key step in mass spectrum metabolism research. In the past two decades, various data analysis strategies and tools have been proposed to interpret biological differences in data, revealing their potential biophysiological pathophysiological mechanisms. Traditional data analysis strategies are concentrated in id...

AI Technical Summary

Problems solved by technology

Existing pathway analysis methods often average the disturbance scores of overlapping metabolites in each pathway involved, thereby increasing the false positive rate of the analysis results; In the case of multi-channel joint modeling, it is difficult to accurately estimate the weight coefficient of the channel; in addition, due to the limited sample size of metabolomics research, the analysis results often have a certain degree of randomness

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