Statistical Analysis of Next Generation Sequencing Data / edited by Somnath Datta, Dan Nettleton.

Format:

Book

Contributor:

Datta, Somnath., Editor.

Nettleton, Dan, Editor.

Series:

Frontiers in Probability and the Statistical Sciences, 2624-9987

Language:

English

Subjects (All):

Statistics.

Human genetics.

Cancer--Research.

Cancer.

Statistics for Life Sciences, Medicine, Health Sciences.

Human Genetics.

Cancer Research.

Local Subjects:

Statistics for Life Sciences, Medicine, Health Sciences.

Human Genetics.

Cancer Research.

Physical Description:

1 online resource (438 p.)

Edition:

1st ed. 2014.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2014.

Language Note:

English

Summary:

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.

Contents:

Statistical Analyses of Next Generation Sequencing Data: An Overview

Using RNA-seq Data to Detect Differentially Expressed Genes

Differential Expression Analysis of Complex RNA-seq Experiments Using edgeR

Analysis of Next Generation Sequencing Data Using Integrated Nested Laplace Approximation (INLA)

Design of RNA Sequencing Experiments

Measurement, Summary, and Methodological Variation in RNA-sequencing

Functional PCA for differential expression testing with RNA-seq data

Mapping of Expression Quantitative Trait Loci using RNA-seq Data

The Role of Spike-In Standards in the Normalization of RNA-seq

Cluster Analysis of RNA-sequencing Data

Classification of RNA-seq Data

Isoform Expression Analysis Based on RNA-seq Data

RNA Isoform Discovery Through Goodness of Fit Diagnostics

MOSAiCS-HMM: A Model-based Approach for Detecting Regions of Histone Modifications from ChIP-seq Data

Hierarchical Bayesian Models for ChIP-Seq Data

Genotype Calling and Haplotype Phasing from Next Generation Sequencing Data.- Analysis of Metagenomic Data

Detecting Copy Number Changes and Structural Rearrangements using DNA Sequencing

Statistical Methods for the Analysis of Next Generation Sequence Data from Paired Tumor-Normal Samples

Statistical Considerations in the Analysis of Rare Variants.

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

ISBN:

3-319-07212-9