Pre Processing And Statistical Inference Methods For High Throughput Genomic Data With Application To Biomarker Detection And Regenerative Medicine

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Pre-processing and Statistical Inference Methods for High-throughput Genomic Data with Application to Biomarker Detection and Regenerative Medicine

Author : Jeea Choi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (979 download)

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Book Synopsis Pre-processing and Statistical Inference Methods for High-throughput Genomic Data with Application to Biomarker Detection and Regenerative Medicine by : Jeea Choi

Download or read book Pre-processing and Statistical Inference Methods for High-throughput Genomic Data with Application to Biomarker Detection and Regenerative Medicine written by Jeea Choi and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome research advances of the last two decades allow us to obtain various forms of data, such as next-generation sequencing, genotyping, phenotyping, as well as clinical information. However, our ability to derive useful information from these data remains to be improved. This motivated me to develop a pipeline with new computational methods. In this dissertation, I develop, implement, evaluate, and apply statistical and computational methods for high-dimensional data analysis to facilitate efforts in regenerative medicine and to uncover novel insights in cancer genomics. The first method is an integrative pathway-index (IPI) model to identify a clinically actionable biomarker of high-risk advanced ovarian cancer patients. Despite improvements in operative management and therapies, overall survival rates in advanced ovarian cancer have remained largely unchanged over the past three decades. The IPI model is applied to messenger RNA expression and survival data collected on ovarian cancer patients as part of the Cancer Genome Atlas project. The approach identifies signatures that are strongly associated with overall and progression-free survival, and also identifies group of patients who may benefit from enhanced adjuvant therapy. The second method is called SCDC for removing increased variability due to oscillating genes in a snapshot scRNA-seq experiment. Single-cell RNA sequencing provides a new avenue for studying oscillatory gene expression. However, in many studies, oscillations (e.g., cell cycle) are not of interest, and the increased variability imposed by them masks the effects of interest. In bulk RNA-seq, the increase in variability caused by oscillatory genes is mitigated by averaging over thousands of cells. However, in typical unsynchronized scRNA-seq, this variability remains. Simulation and case studies demonstrate that by removing increased variability due to oscillations, both the power and accuracy of downstream analysis is increased. Finally, in this thesis, we have extended a data analysis pipeline for both single- cell and bulk RNA-seq data. In this pipeline, we review current standards and resources for (sc)RNA-seq data analysis and provide an extended pipeline that incorporates a quality control scheme and user friendly advanced statistical analysis software for visualization and projected principal component analysis (PCA).

Statistical Analysis of Next Generation Sequencing Data

Author : Somnath Datta
Publisher : Springer
ISBN 13 : 9783319379050
Total Pages : 0 pages
Book Rating : 4.3/5 (79 download)

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Book Synopsis Statistical Analysis of Next Generation Sequencing Data by : Somnath Datta

Download or read book Statistical Analysis of Next Generation Sequencing Data written by Somnath Datta and published by Springer. This book was released on 2016-09-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.

Computational Methods for the Analysis of Genomic Data and Biological Processes

Author : Francisco A. Gómez Vela
Publisher : MDPI
ISBN 13 : 3039437712
Total Pages : 222 pages
Book Rating : 4.0/5 (394 download)

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Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela

Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by MDPI. This book was released on 2021-02-05 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.

High-Performance In-Memory Genome Data Analysis

Author : Hasso Plattner
Publisher : Springer Science & Business Media
ISBN 13 : 3319030353
Total Pages : 239 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis High-Performance In-Memory Genome Data Analysis by : Hasso Plattner

Download or read book High-Performance In-Memory Genome Data Analysis written by Hasso Plattner and published by Springer Science & Business Media. This book was released on 2013-11-19 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent achievements in hardware and software developments have enabled the introduction of a revolutionary technology: in-memory data management. This technology supports the flexible and extremely fast analysis of massive amounts of data, such as diagnoses, therapies, and human genome data. This book shares the latest research results of applying in-memory data management to personalized medicine, changing it from computational possibility to clinical reality. The authors provide details on innovative approaches to enabling the processing, combination, and analysis of relevant data in real-time. The book bridges the gap between medical experts, such as physicians, clinicians, and biological researchers, and technology experts, such as software developers, database specialists, and statisticians. Topics covered in this book include - amongst others - modeling of genome data processing and analysis pipelines, high-throughput data processing, exchange of sensitive data and protection of intellectual property. Beyond that, it shares insights on research prototypes for the analysis of patient cohorts, topology analysis of biological pathways, and combined search in structured and unstructured medical data, and outlines completely new processes that have now become possible due to interactive data analyses.

Statistical and Computational Methods for Analyzing High-Throughput Genomic Data

Author : Jingyi Li
Publisher :
ISBN 13 :
Total Pages : 226 pages
Book Rating : 4.:/5 (858 download)

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Book Synopsis Statistical and Computational Methods for Analyzing High-Throughput Genomic Data by : Jingyi Li

Download or read book Statistical and Computational Methods for Analyzing High-Throughput Genomic Data written by Jingyi Li and published by . This book was released on 2013 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the burgeoning field of genomics, high-throughput technologies (e.g. microarrays, next-generation sequencing and label-free mass spectrometry) have enabled biologists to perform global analysis on thousands of genes, mRNAs and proteins simultaneously. Extracting useful information from enormous amounts of high-throughput genomic data is an increasingly pressing challenge to statistical and computational science. In this thesis, I will address three problems in which statistical and computational methods were used to analyze high-throughput genomic data to answer important biological questions. The first part of this thesis focuses on addressing an important question in genomics: how to identify and quantify mRNA products of gene transcription (i.e., isoforms) from next-generation mRNA sequencing (RNA-Seq) data? We developed a statistical method called Sparse Linear modeling of RNA-Seq data for Isoform Discovery and abundance Estimation (SLIDE) that employs probabilistic modeling and L1 sparse estimation to answer this ques- tion. SLIDE takes exon boundaries and RNA-Seq data as input to discern the set of mRNA isoforms that are most likely to present in an RNA-Seq sample. It is based on a linear model with a design matrix that models the sampling probability of RNA-Seq reads from different mRNA isoforms. To tackle the model unidentifiability issue, SLIDE uses a modified Lasso procedure for parameter estimation. Compared with existing deterministic isoform assembly algorithms, SLIDE considers the stochastic aspects of RNA-Seq reads in exons from different isoforms and thus has increased power in detecting more novel isoforms. Another advantage of SLIDE is its flexibility of incorporating other transcriptomic data into its model to further increase isoform discovery accuracy. SLIDE can also work downstream of other RNA-Seq assembly algorithms to integrate newly discovered genes and exons. Besides isoform discovery, SLIDE sequentially uses the same linear model to estimate the abundance of discovered isoforms. Simulation and real data studies show that SLIDE performs as well as or better than major competitors in both isoform discovery and abundance estimation. The second part of this thesis demonstrates the power of simple statistical analysis in correcting biases of system-wide protein abundance estimates and in understanding the rela- tionship between gene transcription and protein abundances. We found that proteome-wide surveys have significantly underestimated protein abundances, which differ greatly from previously published individual measurements. We corrected proteome-wide protein abundance estimates by using individual measurements of 61 housekeeping proteins, and then found that our corrected protein abundance estimates show a higher correlation and a stronger linear relationship with mRNA abundances than do the uncorrected protein data. To estimate the degree to which mRNA expression levels determine protein levels, it is critical to measure the error in protein and mRNA abundance data and to consider all genes, not only those whose protein expression is readily detected. This is a fact that previous proteome-widely surveys ignored. We took two independent approaches to re-estimate the percentage that mRNA levels explain in the variance of protein abundances. While the percentages estimated from the two approaches vary on different sets of genes, all suggest that previous protein-wide surveys have significantly underestimated the importance of transcription. In the third and final part, I will introduce a modENCODE (the Model Organism ENCyclopedia Of DNA Elements) project in which we compared developmental stages, tis- sues and cells (or cell lines) of Drosophila melanogaster and Caenorhabditis elegans, two well-studied model organisms in developmental biology. To understand the similarity of gene expression patterns throughout their development time courses is an interesting and important question in comparative genomics and evolutionary biology. The availability of modENCODE RNA-Seq data for different developmental stages, tissues and cells of the two organisms enables a transcriptome-wide comparison study to address this question. We undertook a comparison of their developmental time courses and tissues/cells, seeking com- monalities in orthologous gene expression. Our approach centers on using stage/tissue/cell- associated orthologous genes to link the two organisms. For every stage/tissue/cell in each organism, its associated genes are selected as the genes capturing specific transcriptional activities: genes highly expressed in that stage/tissue/cell but lowly expressed in a few other stages/tissues/cells. We aligned a pair of D. melanogaster and C. elegans stages/tissues/cells by a hypergeometric test, where the test statistic is the number of orthologous gene pairs associated with both stages/tissues/cells. The test is against the null hypothesis that the two stages/tissues/cells have independent sets of associated genes. We first carried out the alignment approach on pairs of stages/tissues/cells within D. melanogaster and C. elegans respectively, and the alignment results are consistent with previous findings, supporting the validity of this approach. When comparing fly with worm, we unexpectedly observed two parallel collinear alignment patterns between their developmental timecourses and several interesting alignments between their tissues and cells. Our results are the first findings regarding a comprehensive comparison between D. melanogaster and C. elegans time courses, tissues and cells.

Gene Expression Data Analysis

Author : Pankaj Barah
Publisher : CRC Press
ISBN 13 : 1000425738
Total Pages : 379 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Gene Expression Data Analysis by : Pankaj Barah

Download or read book Gene Expression Data Analysis written by Pankaj Barah and published by CRC Press. This book was released on 2021-11-21 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences

Multiple Testing Procedures with Applications to Genomics

Author : Sandrine Dudoit
Publisher : Springer
ISBN 13 : 9781441923790
Total Pages : 0 pages
Book Rating : 4.9/5 (237 download)

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Book Synopsis Multiple Testing Procedures with Applications to Genomics by : Sandrine Dudoit

Download or read book Multiple Testing Procedures with Applications to Genomics written by Sandrine Dudoit and published by Springer. This book was released on 2010-11-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics.

Computational Methods for Next Generation Sequencing Data Analysis

Author : Ion Mandoiu
Publisher : John Wiley & Sons
ISBN 13 : 1118169484
Total Pages : 460 pages
Book Rating : 4.1/5 (181 download)

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Book Synopsis Computational Methods for Next Generation Sequencing Data Analysis by : Ion Mandoiu

Download or read book Computational Methods for Next Generation Sequencing Data Analysis written by Ion Mandoiu and published by John Wiley & Sons. This book was released on 2016-10-03 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

Statistical Methods for High Throughput Genomics

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (68 download)

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Book Synopsis Statistical Methods for High Throughput Genomics by :

Download or read book Statistical Methods for High Throughput Genomics written by and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The advancement of biotechnologies has led to indispensable high-throughput techniques for biological and medical research. Microarray is applied to monitor the expression levels of thousands of genes simultaneously, while flow cytometry (FCM) offers rapid quantification of multi-parametric properties for millions of cells. In this thesis, we develop approaches based on mixture modeling to deal with the statistical issues arising from both high-throughput biological data sources. Inference about differential expression is a typical objective in analysis of gene expression data. The use of Bayesian hierarchical gamma-gamma and lognormal-normal models is popular for this type of problem. Some unrealistic assumptions, however, have been made in these frameworks. In view of this, we propose flexible forms of mixture models based on an empirical Bayes approach to extend both frameworks so as to release the unrealistic assumptions, and develop EM-type algorithms for parameter estimation. The extended frameworks have been shown to significantly reduce the false positive rate whilst maintaining a high sensitivity, and are more robust to model misspecification. FCM analysis currently relies on the sequential application of a series of manually defined 1D or 2D data filters to identify cell populations of interest. This process is time-consuming and ignores the high-dimensionality of FCM data. We reframe this as a clustering problem, and propose a robust model-based clustering approach based on t mixture models with the Box-Cox transformation for identifying cell populations. We describe an EM algorithm to simultaneously handle parameter estimation along with transformation selection and outlier identification, issues of mutual influence. Empirical studies have shown that this approach is well adapted to FCM data, in which a high abundance of outliers and asymmetric cell populations are frequently observed. Finally, in recognition of concern for an efficient automated FCM ana.

Preprocessing Algorithms and Software for Genomic Studies with High-throughput Sequencing Data

Author : Ilya Y. Zhbannikov
Publisher :
ISBN 13 : 9781321738636
Total Pages : 234 pages
Book Rating : 4.7/5 (386 download)

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Book Synopsis Preprocessing Algorithms and Software for Genomic Studies with High-throughput Sequencing Data by : Ilya Y. Zhbannikov

Download or read book Preprocessing Algorithms and Software for Genomic Studies with High-throughput Sequencing Data written by Ilya Y. Zhbannikov and published by . This book was released on 2015 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: DNA sequencing technologies address problems, the solutions of which were not possible before, such as whole genome sequencing or microbial community characterization without pre-cultivation. Current High-Throughput Sequencing (HTS) techniques allow genomic studies in small labs as well as in large genomic centers. Together with modern computational software, HTS becomes a powerful tool, which allows researchers to answer important biological questions in novel ways. Despite the advantages of modern HTS technologies, large amounts of data and accompanying noise in HTS library confound bioinformatic analysis. Data preprocessing is needed in order to prepare data for subsequent analysis. Data preprocessing includes noise removal as well as techniques such as data reduction. In this dissertation I present a set of software tools that may be used in genomic studies in order to prepare HTS data for subsequent bioinformatic analysis. The first two chapters in this dissertation describe preprocessing tools developed for data denoising. In the last two chapters I explore the use of multiple genomic markers in 16S data analysis with a meta-amplicon analysis algorithm, which facilitates usage of all the information that can be obtained with 16S amplicon sequencing. Meta-amplicon analysis represents improvements on current methods used to characterize bacterial composition and community structure.

Unlocking Biomarker Identification - Harnessing AI and ML for Precision Medicine

Author : Sudha M
Publisher : OrangeBooks Publication
ISBN 13 :
Total Pages : 151 pages
Book Rating : 4./5 ( download)

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Book Synopsis Unlocking Biomarker Identification - Harnessing AI and ML for Precision Medicine by : Sudha M

Download or read book Unlocking Biomarker Identification - Harnessing AI and ML for Precision Medicine written by Sudha M and published by OrangeBooks Publication. This book was released on 2024-08-23 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational techniques to analyze genetic data for identifying biomarkers. These biomarkers are crucial for diagnosing diseases, predicting outcomes, and personalizing treatments. The book covers various machine learning algorithms, such as deep learning, support vector machines, and random forests, explaining how they can be applied to genomic datasets. It discusses feature selection methods, data pre-processing, and the challenges of dealing with high-dimensional data. Case studies and real-world applications illustrate the practical aspects. Additionally, the book addresses ethical considerations and data privacy issues. It is an invaluable resource for bioinformaticians, computational biologists, and healthcare professionals seeking to harness machine learning for genomic

Multivariate Statistical Machine Learning Methods for Genomic Prediction

Author : Osval Antonio Montesinos López
Publisher : Springer Nature
ISBN 13 : 3030890104
Total Pages : 707 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Multivariate Statistical Machine Learning Methods for Genomic Prediction by : Osval Antonio Montesinos López

Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Big Data in Omics and Imaging

Author : Momiao Xiong
Publisher : CRC Press
ISBN 13 : 1498725805
Total Pages : 668 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Big Data in Omics and Imaging by : Momiao Xiong

Download or read book Big Data in Omics and Imaging written by Momiao Xiong and published by CRC Press. This book was released on 2017-12-01 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.

Statistical and Computational Methods for Comparing High-Throughput Data from Two Conditions

Author : Xinzhou Ge
Publisher :
ISBN 13 :
Total Pages : 186 pages
Book Rating : 4.:/5 (128 download)

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Book Synopsis Statistical and Computational Methods for Comparing High-Throughput Data from Two Conditions by : Xinzhou Ge

Download or read book Statistical and Computational Methods for Comparing High-Throughput Data from Two Conditions written by Xinzhou Ge and published by . This book was released on 2021 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of high-throughput biological technologies have enabled researchers to simultaneously perform analysis on thousands of features (e.g., genes, genomic regions, and proteins). The most common goal of analyzing high-throughput data is to contrast two conditions, to identify ``interesting'' features, whose values differ between two conditions. How to contrast the features from two conditions to extract useful information from high-throughput data, and how to ensure the reliability of identified features are two increasingly pressing challenge to statistical and computational science. This dissertation aim to address these two problems regarding analysing high-throughput data from two conditions. My first project focuses on false discovery rate (FDR) control in high-throughput data analysis from two conditions. FDR is defined as the expected proportion of uninteresting features among the identified ones. It is the most widely-used criterion to ensure the reliability of the interesting features identified. Existing bioinformatics tools primarily control the FDR based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions, two requirements that are often unmet in biological studies. In Chapter \ref{chap:clipper}, we propose Clipper, a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper is applicable to identifying both enriched and differential features from high-throughput biological data of diverse types. In comprehensive simulation and real-data benchmarking, Clipper outperforms existing generic FDR control methods and specific bioinformatics tools designed for various tasks, including peak calling from ChIP-seq data, and differentially expressed gene identification from bulk or single-cell RNA-seq data. Our results demonstrate Clipper's flexibility and reliability for FDR control, as well as its broad applications in high-throughput data analysis. My second project focuses on alignment of multi-track epigenomic signals from different samples or conditions. The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign can also detect common chromatin state patterns across multiple epigenomes from conditions, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns.

Analysis of High-throughput Genomic Data

Author : Yen-Tsung Huang
Publisher :
ISBN 13 :
Total Pages : 368 pages
Book Rating : 4.:/5 (795 download)

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Book Synopsis Analysis of High-throughput Genomic Data by : Yen-Tsung Huang

Download or read book Analysis of High-throughput Genomic Data written by Yen-Tsung Huang and published by . This book was released on 2012 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt:

A Model-based Framework for High-throughput Genomic Data Enhancement

Author : Dongrui Zhong
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis A Model-based Framework for High-throughput Genomic Data Enhancement by : Dongrui Zhong

Download or read book A Model-based Framework for High-throughput Genomic Data Enhancement written by Dongrui Zhong and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reusability is part of the FAIR data principle, which aims to make data Findable, Accessible, Interoperable, and Reusable. One of the current efforts to increase the reusability of public genomics data has been to focus on the inclusion of quality metadata associated with the data. When necessary metadata are missing, most researchers will consider the data useless. We developed a framework to predict the missing metadata of gene expression datasets to maximize their reusability. We found that when using predicted data to conduct other analyses, it is not optimal to use all the predicted data. Instead, one should only use the subset of data, which can be predicted accurately. We proposed a new metric called Proportion of Cases Accurately Predicted (PCAP), which is optimized in our specifically-designed machine learning pipeline. The new approach performed better than pipelines using commonly used metrics such as F1-score in terms of maximizing the reusability of data with missing values. We also found that different variables might need to be predicted using different machine learning methods and/or different data processing protocols. Using differential gene expression analysis as an example, we showed that when missing variables are accurately predicted, the corresponding gene expression data can be reliably used in downstream analyses. The performance of conventional machine learning methods relies greatly on the quality of the data. In the field of bioinformatics, researchers often need to work with huge amount of data from different resources, such as Gene Expression Omnibus (GEO) and the Cancer Genome Atlas Program (TCGA). It is possible that gene expression data collected in different studies are somewhat inconsistent, for reasons such as the difference in the experiment design or the platform (different microarray or RNA-seq platforms) used to read the gene expression data. This brings difficulty in cross-study analysis, such as model transferability among studies. Some normalization methods such as quantile normalization and rank normalization are widely used to deal with such inconsistency. However, models trained using normalized data could still suffer from extreme values or large variation of errors in the original data, since conventional normalization methods may not completely overcome these issues. We propose to use the pairwise rank information among the gene expression values (paired predictive variables, or PPV) calculated from the original gene expression values to train machine learning models. Our result shows that PPV gives more statistically robust models, while the interpretability of variables is greatly maintained, and the model performance is equivalent or better than models trained using original gene expression data with conventional normalization methods. The raw gene expression values can be decomposed into four components: the true value, systematic errors, random errors, and unexpected errors (producing outliers when large enough). Normalization methods are used to remove systematic errors, and outlier detection methods can be applied to detect outliers. Few, if at all, methods have been developed to remove random errors. In this study, we developed an efficient method based on probabilistic principal component analysis (PPCA) to remove both random and unexpected errors in gene expression data by borrowing information across genes and samples using an internal prediction strategy. The proposed method, gene-expression-data-enhancement (GEDE), first estimates a covariance matrix for all the genes using an existing dataset with a relatively large number of samples. It then infers enhanced gene expressions for a dataset to be analyzed by predicting gene expression values using expression values of other genes. We showed that the enhanced version of a gene expression profile has higher quality than the original profile using both simulation study and real data analysis. In real data analysis, we showed that enhanced gene expression profiles gave better differential gene expression analysis results than original profiles. We recommend gene expression enhancement as an essential step in gene expression data analysis pipeline.

Computational Methods for the Analysis of Genomic Data and Biological Processes

Author : Francisco A. Gómez Vela
Publisher :
ISBN 13 : 9783039437726
Total Pages : 222 pages
Book Rating : 4.4/5 (377 download)

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Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela

Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by . This book was released on 2021 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.