Statistical Analysis of Next Generation Sequencing Data (Hardcover)

Somnath Datta, Dan Nettleton

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商品描述

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.

商品描述(中文翻譯)

下一代测序(Next Generation Sequencing,NGS)是最新的高通量技术,革新了基因组研究。NGS生成了大规模的基因组数据集,对于我们今天所面临的大数据现象起着关键作用。为了从高维度的NGS数据中提取信号并进行有效的统计推断和预测,需要新颖的数据分析和统计技术。本书包含了20章,由与NGS数据合作的著名统计学家撰写。主题涵盖了从NGS数据的基本预处理和分析到更复杂的基因组应用,如拷贝数变异和异构表达检测。希望了解这个快速发展且令人兴奋的领域的研究统计学家会发现本书很有用。此外,本书的许多章节可以用于统计生物信息学的研究生课程,培养未来将需要处理基因组数据的生物统计学家,他们将在基础生物医学研究、基因组临床试验和个体化医学中发挥作用。

关于编辑:
Somnath Datta是路易斯维尔大学生物信息学和生物统计学的教授和副主席。他是美国统计协会的会士、数理统计学会的会士和国际统计学会的选举会员。他在统计学、生物统计学和生物信息学的许多研究领域做出了贡献。

Dan Nettleton是爱荷华州立大学统计学系的生物统计学劳伦斯·H·贝克特讲座教授。他是美国统计协会的会士,并在统计学、生物学和生物信息学的各种主题上发表了研究成果。