Coronary Heart Disease Metabolomics Data Processing And Analysis

Metabonomics is a technology developed in 1990s, which takes qualitative and quantitative analysis research on a biological or endogenous cell metabolites. Being a discipline emerging in recent years, metabonomics is a rapid development "omics" technology in the "post-genomics" period, and is an important component and basic of systems biology, it has become a hot research area just as proteomics, transcriptomics and genomics. The development of various chemical analysis techniques, metrology and statistics promote the development of metabonomics and the expansion and deepening of metabonomics application areas.Metabonomics is the result of proteome, transcriptome and gene overall expression, directly reflects the biochemical state of the organization, can sensitively describe the changes in physiological and pathological conditions of body, so it has an important significance for clarifying the complex system of life. Metabonomics has become an international hot spot on the major of disease and health research. Thus, using metabonomics method to the study complex diseases has important intervention significance in the early diagnosis of disease, disease prevention and drug intervention.Objective of this paper is understanding the metabonomics research and development process, the clinical research application and its development trend through literature research, besides, applying several commonly used statistical methods in metabonomics data and probing their usage in metabonomic data process.In this paper, it elaborates the evolution of metabonomics concept and its development progress, the advantages of metabonomics and metabonomics research policy trends. Compared with other several groups, Metabonomics highlights great advantages, that is just the reason that metabonomics becomes more and more popular. Metabolomics research has tends to integrate, quantification and standardization, especially the methods, tools, variable selection and so on of metabonomics data processing methods.Metabonomics data processing is a critical step in metabonomics analysis and research process, in this step, you can make a final interpretation and instructions of the data, and get clear and valuable information. Because the requirements of metabonomics technology and cost, Data obtained in metabonomics research usually have small sample size and large independent variables (study factor) numbers characteristics. Therefore, according to the characteristics of metabonomic data, selecting the appropriate data processing methods (statistical methods) is necessary. However, metabonomics has not had standard and uniform method of data processing currently. Therefore, according to the specific application, exploring metabonomics analysis tools and methods is what quite a lot of researchers appeal to, and that is also a research with some practical value.Metabolomics applications in disease research are quite a lot, and are deeper than other areas. On the base of relevant metabonomics theories and methods study, this paper makes metabolomics data statistical analysis of the coronary heart disease with Qi PBSS. It applies SPSS17.0 software in statistical analysis process. Methods:(1) using two-sample t test, make single factor analysis for Coronary Heart Disease Qi PBSS group and comparison group; (2) Making Spearman correlation analysis for the two groups; (3) Logistic regression analysis; (4) Making principle component analysis (PCA)for Coronary Heart Disease Qi PBSS group and comparison group respect ively; (5) Neural network analysis. Results:(1) Through t test and Spearman correlation analysis, ribosomal alcohol,1-deoxy-glucose, glucose oxime, D-glucose acid, palmitic acid, inositol, heptadecanoic acid, stearic acid,4-hydroxy-pyrimidine, Citric acid hydrochloride, Xin acid and other components 16 metabolites were significantly different (correlated), these several metabolites in the coronary heart disease with Qi PBSS group are higher than that in the caparison group, and they are positively correlated with coronary heart disease with Qi PBSS; (2) Logistic regression analysis showed that high levels of stearic acid deficiency is the risk factor of coronary heart disease with Qi PBSS; (3) Principal component analysis showed that palmitic acid, stearic acid,4-hydroxy pyrimidine and heptadecanoic acid are higher in coronary heart disease with Qi PBSS group. In summary, based on the above analysis, we can able to see the risk factors of coronary heart disease with Qi PBSS contains:ribose alcohol,1-deoxy-glucose, glucose oxime, D-glucose acid, palmitic acid, inositol, heptadecanoic acid, Stearic acid,4-hydroxy-pyrimidine, lemon hydrochloride acid, acrid acid components of high content of these metabolites, in particular, stearic acid, in some ways are more obvious. In addition, the phosphate low content is also risk factor for coronary heart disease. However, there are a few unknown metabolites are included in them.In the analysis, using a "single variable-the two variables multiple variables" analysis in-depth step by step, from simple to complex, has a clear thinking. Finally, integrating several methods gets a relatively good analysis result. Comparing several analysis methods, this paper thinks, in metabolomics data analysis, we can combine t test or Spearman correlation with principal component analysis. Of course, in big sample volume, multi-variable analysis, neural networks analysis can be used.Using a variety of data analysis tools and applying metabonomics detection technology, through analysis and synthesis of metabolites, can study disease pathogenesis in-depth, so it has important application value.