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Statistical Methods For High Throughput Genomics
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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.
Book Synopsis Computational Genomics with R by : Altuna Akalin
Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
Book Synopsis Optimal High-Throughput Screening by : Xiaohua Douglas Zhang
Download or read book Optimal High-Throughput Screening written by Xiaohua Douglas Zhang and published by Cambridge University Press. This book was released on 2011-02-21 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This concise, self-contained and cohesive book focuses on commonly used and recently developed methods for designing and analyzing high-throughput screening (HTS) experiments from a statistically sound basis. Combining ideas from biology, computing and statistics, the author explains experimental designs and analytic methods that are amenable to rigorous analysis and interpretation of RNAi HTS experiments. The opening chapters are carefully presented to be accessible both to biologists with training only in basic statistics and to computational scientists and statisticians with basic biological knowledge. Biologists will see how new experiment designs and rudimentary data-handling strategies for RNAi HTS experiments can improve their results, whereas analysts will learn how to apply recently developed statistical methods to interpret HTS experiments.
Book Synopsis Statistical Methods for the Analysis of Genomic Data by : Hui Jiang
Download or read book Statistical Methods for the Analysis of Genomic Data written by Hui Jiang and published by MDPI. This book was released on 2020-12-29 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.
Book Synopsis High-dimensional Data Analysis by : Tony Cai;Xiaotong Shen
Download or read book High-dimensional Data Analysis written by Tony Cai;Xiaotong Shen and published by . This book was released on with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last few years, significant developments have been taking place in highdimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. This book intends to examine important issues arising from highdimensional data analysis to explore key ideas for statistical inference and prediction. It is structured around topics on multiple hypothesis testing, feature selection, regression, cla.
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 2014-07-03 with total page 438 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.
Author :Naiara Rodríguez-Ezpeleta Publisher :Springer Science & Business Media ISBN 13 :1461407826 Total Pages :258 pages Book Rating :4.4/5 (614 download)
Book Synopsis Bioinformatics for High Throughput Sequencing by : Naiara Rodríguez-Ezpeleta
Download or read book Bioinformatics for High Throughput Sequencing written by Naiara Rodríguez-Ezpeleta and published by Springer Science & Business Media. This book was released on 2011-10-26 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next generation sequencing is revolutionizing molecular biology. Owing to this new technology it is now possible to carry out a panoply of experiments at an unprecedented low cost and high speed. These go from sequencing whole genomes, transcriptomes and small non-coding RNAs to description of methylated regions, identification protein – DNA interaction sites and detection of structural variation. The generation of gigabases of sequence information for each of this huge bandwidth of applications in just a few days makes the development of bioinformatics applications for next generation sequencing data analysis as urgent as challenging.
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.
Book Synopsis Statistical Methods for the Analysis of Genomic Data by : Hui Jiang
Download or read book Statistical Methods for the Analysis of Genomic Data written by Hui Jiang and published by . This book was released on 2020 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.
Book Synopsis Statistical Methods and Software for High-throughput Gene Expression Experiments by : James Hudson Bullard
Download or read book Statistical Methods and Software for High-throughput Gene Expression Experiments written by James Hudson Bullard and published by . This book was released on 2009 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing by : Ana M. Aransay
Download or read book Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing written by Ana M. Aransay and published by Springer. This book was released on 2016-06-02 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: High throughput sequencing (HTS) technologies have conquered the genomics and epigenomics worlds. The applications of HTS methods are wide, and can be used to sequence everything from whole or partial genomes, transcriptomes, non-coding RNAs, ribosome profiling, to single-cell sequencing. Having such diversity of alternatives, there is a demand for information by research scientists without experience in HTS that need to choose the most suitable methodology or combination of platforms and to define their experimental designs to achieve their specific objectives. Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing aims to collect in a single volume all aspects that should be taken into account when HTS technologies are being incorporated into a research project and the reasons behind them. Moreover, examples of several successful strategies will be analyzed to make the point of the crucial features. This book will be of use to all scientist that are unfamiliar with HTS and want to incorporate such technologies to their research.
Book Synopsis Statistical Methods for High-dimensional Genomic Data by : Michael Chiao-An Wu
Download or read book Statistical Methods for High-dimensional Genomic Data written by Michael Chiao-An Wu and published by . This book was released on 2009 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput genomic studies hold great promise for providing insight into key biological and medical problems, but the high-dimensionality of the data from these studies constitutes a great challenge for researchers. This thesis seeks to address some of the methodological challenges posed by high-dimensional genomic data. First, the need to develop accurate classifiers based on genomic markers motivated the development of sparse linear discriminant analysis (sLDA), a regularized form of linear discriminant analysis, which performs simultaneous classification and variable selection. The second and third chapters of this thesis are concerned with multifeature testing. In the gene expression setting, we apply sLDA to test for differential expression of gene pathways by using the sLDA weights to reduce each pathway to a univariate score which may be evaluated via permutation. Then for genome wide association studies, we consider using the logistic kernel machine based testing framework to evaluate the significance of SNPs grouped on the basis of proximity to known genomic features. Finally, in the last chapter we study the use of sparse regularized regression for making inference in high dimensional data. Specifically, we develop a parametric permutation test based on the LASSO estimator for testing the effect of individual markers in "omics" settings.
Book Synopsis Statistical Diagnostics for Cancer by : Matthias Dehmer
Download or read book Statistical Diagnostics for Cancer written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-11-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.
Book Synopsis Methods in Statistical Genomics by : Philip Chester Cooley
Download or read book Methods in Statistical Genomics written by Philip Chester Cooley and published by RTI Press. This book was released on 2016-08-29 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this book is to describe procedures for analyzing genome-wide association studies (GWAS). Some of the material is unpublished and contains commentary and unpublished research; other chapters (Chapters 4 through 7) have been published in other journals. Each previously published chapter investigates a different genomics model, but all focus on identifying the strengths and limitations of various statistical procedures that have been applied to different GWAS scenarios.
Book Synopsis Statistical Methods for Normalization and Analysis of High-throughput Genomic Data by : Tobias Guennel
Download or read book Statistical Methods for Normalization and Analysis of High-throughput Genomic Data written by Tobias Guennel and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput genomic datasets obtained from microarray or sequencing studies have revolutionized the field of molecular biology over the last decade. The complexity of these new technologies also poses new challenges to statisticians to separate biological relevant information from technical noise. Two methods are introduced that address important issues with normalization of array comparative genomic hybridization (aCGH) microarrays and the analysis of RNA sequencing (RNA-Seq) studies. Many studies investigating copy number aberrations at the DNA level for cancer and genetic studies use comparative genomic hybridization (CGH) on oligo arrays. However, aCGH data often suffer from low signal to noise ratios resulting in poor resolution of fine features. Bilke et al. showed that the commonly used running average noise reduction strategy performs poorly when errors are dominated by systematic components. A method called pcaCGH is proposed that significantly reduces noise using a non-parametric regression on technical covariates of probes to estimate systematic bias. Then a robust principal components analysis (PCA) estimates any remaining systematic bias not explained by technical covariates used in the preceding regression. The proposed algorithm is demonstrated on two CGH datasets measuring the NCI-60 cell lines utilizing NimbleGen and Agilent microarrays. The method achieves a nominal error variance reduction of 60%-65% as well as an 2-fold increase in signal to noise ratio on average, resulting in more detailed copy number estimates. Furthermore, correlations of signal intensity ratios of NimbleGen and Agilent arrays are increased by 40% on average, indicating a significant improvement in agreement between the technologies. A second algorithm called gamSeq is introduced to test for differential gene expression in RNA sequencing studies. Limitations of existing methods are outlined and the proposed algorithm is compared to these existing algorithms. Simulation studies and real data are used to show that gamSeq improves upon existing methods with regards to type I error control while maintaining similar or better power for a range of sample sizes for RNA-Seq studies. Furthermore, the proposed method is applied to detect differential 3' UTR usage.
Book Synopsis Handbook of Statistical Genomics by : David J. Balding
Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1828 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.
Book Synopsis Statistical Theory and Methods for Evolutionary Genomics by : Xun Gu
Download or read book Statistical Theory and Methods for Evolutionary Genomics written by Xun Gu and published by OUP Oxford. This book was released on 2010-11-04 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution.
