Title Study of unobserved factors in fatty acids with exploratory data analysis Author(s) 陳, 一凡 Citation 北海道大学. 博士(情報科学) 甲第14176号 Issue Date 2020-06-30 DOI 10.14943/doctoral.k14176 Doc URL http://hdl.handle.net/2115/79055 Type theses (doctoral) File Information Yifan_Chen.pdf Instructions for use Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP Study of Unobserved Factors in Fatty Acids with Exploratory Data Analysis Yifan Chen 1 Contents 1. Introduction .................................................................................................................. 4 1.1 Background of our study ...................................................................................... 4 1.2 The purpose of our study ..................................................................................... 6 1.3 The structure of this study................................................................................... 8 2. Knowledge of fatty acids ............................................................................................... 9 2.1 Chain length of fatty acids ................................................................................... 9 2.2 Saturation .......................................................................................................... 11 2.3 Triglyceride ....................................................................................................... 15 2.4 Vitamin D .......................................................................................................... 16 2.5 Digestion and nutrition ..................................................................................... 17 2.6 Lipids and health ............................................................................................... 26 3. Knowledge of exploratory data analysis ...................................................................... 33 3.1 Functional data analysis ..................................................................................... 33 3.1.1 Functionalization ..................................................................................... 33 3.1.2 Functional clustering ............................................................................... 41 3.1.3 Time series data analysis ......................................................................... 43 3.2 Dimension reduction methods .......................................................................... 45 3.2.1 Principal component analysis .................................................................. 45 3.2.2 Factor analysis ......................................................................................... 45 3.2.3 Independent component analysis ............................................................ 48 2 3.2.4 Common principal component analysis .................................................. 49 4. Analysis on serum fatty acid dataset ........................................................................... 53 4.1 Serum fatty acid dataset ..................................................................................... 53 4.2 Analysis on free fatty acid and total fatty acid dataset ....................................... 57 4.2.1 Purpose .................................................................................................... 57 4.2.2 Result of analysis ..................................................................................... 57 4.2.3 Conclusion ............................................................................................... 71 4.3 Analysis on serum cholesteryl ester dataset ....................................................... 73 4.3.1 Purpose .................................................................................................... 73 4.2.2 Results of analysis .................................................................................... 74 4.3.4 Conclusion ............................................................................................... 81 4.4 Analysis on common component analysis ......................................................... 82 4.4.1 Purpose .................................................................................................... 82 4.4.2 Result of analysis ..................................................................................... 82 4.4.3 Conclusion ............................................................................................... 84 5. Analysis on milk fatty acid dataset .............................................................................. 85 5.1 Milk fatty acid dataset ........................................................................................ 85 5.2 Method of analysis ............................................................................................. 87 5.3 Result of analysis ............................................................................................... 92 5.4 Conclusion ......................................................................................................... 94 6. Conclusion ................................................................................................................ 104 3 1. Introduction 1.1 Background of our study Fatty acid plays an important role in human health and fat-related diseases. Fatty acids can be obtained from various foods or biosynthesized in the body (Fig. 1.1). Food lipids are digested and absorbed at intestine, and transferred via circulation to various organs. Cells contain various lipid pools in organelle. Lipids are exchanged between organelle via intracellular lipid traffics and metabolism. Organs contain unique lipid constituents, and exchange them with plasma lipoproteins across cell membranes. Lipoproteins consist of various lipoprotein particles having unique lipid compositions, such as chylomicrons, very-low-density lipoproteins (VLDL), low-density lipoproteins (LDL), and high-density lipoproteins (HDL). Lipids are exchanged among lipoprotein particles and free fatty acids associated with albumin in circulation. Thus, plasma lipids reflect complex lipid pools and metabolism in the body. Fig. 1.1 Various lipid pools and their relationship 4 Currently, fatty acids are regarded as one of the most important molecules in various pathological conditions, they can provide us energy, cholesterol or other lipids, and they also have signaling function, and help cells interface. Therefore, fatty acids have an essential relationship with our body and health condition. By studying on fatty acids, we can have a good understanding on our health condition. Adjusting fatty acids intake in a scientific way is an economic and significant way to control our health condition (prevent and manage diseases), especially in remote and not well-developed areas. Fatty acids are classified into free fatty acids and esterified fatty acids. Esterified fatty acids are further divided into cholesterol esters (CE), triglycerides, and phospholipids. According to the carbon chain-length and the number and location of double bond, CEs and triglycerides have also diversity in the structure. Moreover, fatty acids undergo various metabolic processes including dietary intake, enteric absorption, plasma lipoprotein metabolism, enzymatic modification (esterification, cleavage, elongation and desaturation of fatty acyl chain), and secretion as triglyceride in milk. Because of complexity of fatty acid, characteristics features of fatty acids in biological samples naturally generates a high-dimension dataset. Due to the variety and complexity in fatty acid dataset, it is hardly possible to extract unobserved but important factors in dataset. Therefore, it is necessary to use multivariate analysis to extract the unobserved factors from the dataset. Exploratory data analysis is a procedure to interpret and analyze data to give information for further data study and gathering. We can find latent factors in dataset by exploratory data analysis with more ease. In exploratory data analysis, there are many classical methods, such as principal component analysis, factor analysis, and independent component analysis. It is not sufficient to induce unobserved factors only with classical methods, thus advanced methods, such as functional regression analysis and functional clustering, common principal component analysis are conducted. Exploratory data analysis is a large conception consisting of many statistical methods to deal with various dataset and give further information on more accurate data feature interpretation. 5 Therefore, exploratory data analysis has become important in analytical and clinical chemistry, especially for analyses of large and complicated biological or medical datasets generated from comprehensive mass spectrometry. Previous epidemiological studies on fatty acid metabolism have not utilized exploratory data analysis, but have carried out only simple statistic approaches. For instance, fatty acid composition of CEs has been studied with special interests to 1) its correlation with other lipids or other sample characteristics such as gender effect and ageing (Chen, Z., et al., 2019), and 2) estimation of the risk for myocardial infarction (Öhrvall, M., et al., 1996) and diabetes (Vessby, B., et al., 1994), 3) search for new biomarkers of cancer (Zhang, Y., et al., 2016) and inflammation (Khorsan, R., et al., 2014), and 4) simple discriminant analysis of two or more groups of dataset (Jung, Y., et al., 2018). However, previous studies including the above literatures