Integration system and method for correlation analysis among multiple omics data sets

Abstract

The invention provides an integration system and method for correlation analysis among multiple omics data sets. The method comprises the following steps: processing each type of omics original data to obtain an omics data file corresponding to the type of the omics original data, and processing phenotypic original data to obtain a phenotypic data file; adding two types of omics variables in the omics variables of the various types of omics data files into a variable set, and combining the omics variables in the variable set in pairs to generate a plurality of omics single-dimensional data pairs; performing dimension reduction processing on each type of omics data file to obtain a combined variable corresponding to the type; combining the various types of combined variables in pairs to generate a plurality of combined variable data pairs; combining the various types of combined variables with the phenotypic data files in pairs to generate a plurality of omics and phenotypic data pairs;and performing correlation analysis on the omics single-dimensional data pairs, the combined variable data pairs and the omics and phenotype data pairs to respectively obtain respective correlation relationships and corresponding correlation parameters, and outputting the respective correlation relationships and the corresponding correlation parameters. The invention provides an effective methodfor comprehensively analyzing the biological information of the system and is capable of discovering the linear correlation relationship among the multiple omics data and finding out the nonlinear correlation pair with the function relationship and without the function relationship, thereby having comprehensive functionality.

Description

technical field [0001] The invention relates to the field of biological multi-omics calculation analysis, in particular to an integrated system and method for correlation analysis among multi-omics data sets. Background technique [0002] High-throughput omics data from multiple levels and sources such as metabolites, microbiomes, transcriptomes, proteomes, and genomes has a huge volume and diverse data structures, and the relationship between variables and between variables and phenotypes is intricate. By integrating multi-omics and phenotypic data, it is helpful to find effective association pairs between a large amount of complex multi-omics data or between omics and phenotypic data, revealing the interaction and relationship between components or between components and phenotypes, It is one of the commonly used methods in translational medicine and systems biology research. However, due to the different sources, structures, and characteristics of multi-omics data, it is...

AI Technical Summary

Problems solved by technology

In addition, due to the large amount and complex structure of multi-omics data, the current multi-omics joint analysis makes it difficult to screen a single biomarker, and most of these analysis strategies are limited to the correlation between omics data, ignoring the importance of phenotypic characteristics in multiple The guiding role in the joint analysis of omics does not consider the influence of covariates commonly found in biological problems, and the accuracy and authenticity of the results need to be improved

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