계층적 구조 모형을 이용한 miRNA, mRNA 발현 자료의 통합분석

학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 통계학과, 2018. 8. 박태성.Identification of multi-markers is one of the most challenging issues in this new era of personalized medicine. Although many methods have been developed to identify candidate markers for each type of omics data, few can facilitate multi-marker identification. It is well known that microRNAs (miRNAs) affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration (HisCoM-mimi) model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In this thesis, we suggest two types of HisCoM-mimi. First type of HisCoM-mimi is used for discriminant analysis. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods. Second type of HisCoM-mimi is used for survival analysis (mimi-surv). As the result of comparison study of HisCoM-mimi for discriminant analysis, we found the statistical power of mimi-surv to be better than other models in simulated comparisons. In analysis of real clinical data, mimi-surv successfully identified miRNA-mRNA integrations sets associated with progression-free survival of PDAC patients. Interestingly, miR-93, a previously unidentified PDAC-related miRNA, was found by mimi-surv, both in patient data from Seoul National University Hospital and The Cancer Genome Atlas (TCGA). Also, methods that use known structure for miRNA-mRNA regularization, found more PDAC related miRNAs than others. As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for diagnosis or prognosis of cancer, providing a much broader biological interpretationAbstract i Contents iii List of Figures v List of Tables vii 1 Introduction 1 1.1 Biological background on omics data analysis 1 1.1.1 Central dogma in biological procedure 2 1.1.2 Definition of miRNA inhibition process 4 1.1.3 Review of transcriptomes measuring techniques 6 1.2 Statistical procedure to analyze omics data 10 1.2.1. Quality control and normalization of microarray 10 1.2.2. Statistical methods for finding significant features 13 1.2.3. Multiple testing problems on Omics data analysis 15 1.2.4. Review of traditional data integration methods 19 1.3 The purpose of this study 20 1.4 Outline of the thesis 20 2 Review of component-based structural equation models 22 2.1 Partial least square path modeling (PLS-PM) 22 2.2 Generalized structured component analysis (GSCA) 25 2.3 Extended Redundancy Analysis (ERA) 28 2.4 Pathway based approach using hierarchical components of collapsed rare variants (PHARAOH) 30 3 Motivating Example 32 3.1 Pancreatic ductal adenocarcinoma (PDAC) 32 3.2 Seoul National University Hospital (SNUH) PDAC samples 33 3.3 The Cancer Genome Atlas (TCGA) PDAC samples 36 4 Hierarchical structural component modeling of microRNA-mRNA integration model for binary phenotype 38 4.1 Introduction 38 4.2 Methods 39 4.2.1 HisCoM-mimi model 39 4.2.2 Fitting the HisCoM-mimi model 44 4.2.3 Comparative models 44 4.2.4 Simulation Study 46 4.3 Results 50 3.3.1 Simulation results 50 3.3.2. Constructing miRNA-mRNA subnetwork 54 3.3.3. Integration analysis for the SNUH PDAC data 54 4.4 Discussion 65 5 Hierarchical structural component miRNA-mRNA integration model for survival phenotype 67 5.1 Introduction 67 5.2 Methods 68 5.2.1 mimi-surv model 68 5.2.2 Fitting the mimi-surv model 70 5.2.3 Comparative model 71 5.2.5 Simulation study 72 5.3 Results 76 5.3.1 Simulation results 76 5.3.2 SNUH dataset analysis results 80 5.3.2 TCGA dataset analysis results 85 5.4 Discussion 87 6 Summary and Conclusions 88 Bibliography 91Docto

전장유전체연구에서의 고차원 유전자-유전자 간의 교호작용 분석

학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2013. 2. 박태성.Genome-wide association studies already have found hundreds of associations between genetic variants and complex human diseases and traits. Most GWA-studies are concentrated in single variants effect size. In this reason, most variants which were found by studies can only explain a small part of human diseases and traits. Consequently, many researchers study gene-gene or gene-environment interactions and develop for these interactions. In 2001, Ritchie et al. proposed MDR method for the determination of gene-by-gene and gene-by-environment interactions. This method has a benefit of fitting and interpreting effects of gene-gene interactions. However this method can only be adopted for case- control data and can’t adjust other environmental variables. To overcoming these disadvantages, in 2007, Xinag-Yang et al. proposed generalized MDR method. Despite of its benefits, this method also has a data sensitive problem. In other words, performance of GMDR method is affected by erroneous samples Erroneous samples means which diverge from the usual tendencies of other samples which expected to be similar with them. In this reason, we first show about negative effects of erroneous samples in GMDR by using a toy example and propose two methods for reducing effects caused by erroneous samples. Methods to reduce effects of erroneous samples which we propose are L-estimator GMDR and M-estimator. L-estimator and M-estimator are statistical methods deriving for robust estimation. In this study, to adjust concepts of L-estimator and M-estimator to GMDR method has advantages in consistency of choices. As a result we reveal that L-estimator GMDR and M-estimator has a benefit to robustness by simulation and real data analysis.1 Introduction 2 Materials and Methods 2.1 KARE Data 2.2 MDR method 2.3 GMDR method 2.4 L-estimator GMDR and M-estimator GMDR 3 Results 3.1 Toy example 3.2 Simulation Results 3.3 Real data analysis 4 Descussion References 국문초록Maste

Implementation of Analog Self-Interference Cancellation for In-band Wireless Full-duplex Communications

동일대역 전이중 무선통신은 같은 주파수에서 동시에 데이터를 송수신하여 주파수 이용효율을 극대화하는 기술이다. 전이중 무선통신에서 자신의 전송신호가 수신신호에 간섭으로 작용하여 수신신호의 신호 대 잡음비를 낮추는 자가간섭이 발생한다. 본 논문에서는 아날로그 방식의 자가간섭 무효화 기술을 software-defined-radio장비를 사용하여 구현하고, 이를 활용해 수행한 전이중 무선통신 실험 결과를 기술한다.에서는 아날로그 방식의 자가간섭 무효화 기술을 software-defined-radio장비를 사용하여 구현하고, 이를 활용해 수행한 전이중 무선통신 실험 결과를 기술한다.2