Universal correction method of large-scale metabonimics data

Abstract

The invention discloses a universal correction method of large-scale metabonimics data. The method comprises the following steps: analyzing samples through adopting a chromatograph-mass spectrometer to obtain a metabolic profiling, calculating a ratio of the response intensities of metabolites in every two adjacent quality control samples (QC), sequencing the obtained ratios from small to large, screening the ratios accounting for 5% of the total quantity of the ratios as discrete points, averagely distributing the 5% discrete points to two ends of the sequenced ratios in order to establish a model and screen the random error in the metabonimics data, ad correcting the random error through using a linear fitting model of the ratios; and constructing a virtual QC technology by using a linear regression model in order to realize the system error correction of a large-scale metabonimics data set. The method has the advantages of highly-efficient and accurate correction of the random error and the system error of the large-scale metabonimics data, realization of integration of multi-batch and different device metabonimics data.

Description

technical field [0001] The present invention relates to the fields of analytical chemistry and metabolomics. is a method for large-scale metabolomics data correction. Background technique [0002] Metabolomics is a discipline that studies the dynamic changes of endogenous small molecule metabolites in organisms, and is another important branch of systems biology after genomics, transcriptomics, and proteomics. Metabolites are the final products of gene regulation and the link between genotype and biological phenotype. The qualitative and quantitative analysis of small molecule metabolites can directly reflect the current physiological state of the body. In recent years, with the development of analytical technology, the application of metabolomics has been extended to the early diagnosis and treatment of diseases, the discovery of clinical markers, drug screening and toxicity evaluation, drug quality control, functional genomics, botany and other life field of scientific r...

AI Technical Summary

Problems solved by technology

However, the analysis of a large number of samples requires a long analysis time, which will cause a decrease in the sensitivity of the instrument. Therefore, it is usually necessary to divide a large number of samples into multiple batches for testing. According to the status of the instrument response between each batch, It is necessary to replace some necessary accessories (such as injection pads, liners, etc.), chromatographic columns and different instruments, etc.

During these operations, there are usually random errors and systematic errors in the metabolic data between different batches, making it difficult to achieve large-scale data integration; in order to realize the integration of multiple batches of data from different batches and different instruments, we A large-scale data correction method is established, which can correct random errors and systematic errors at the same time, realize the integration of multiple batches, and meet the requirements of large-scale metabolomics analysis

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