Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods

Statisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research.

Key Features:

* dice games and exercises, including one using interactive software, for teaching the concepts in the classroom

* examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data

* gradual introduction to the mathematical equations needed

* how to choose between different methods of multiple hypothesis testing

* how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates

* guidance through the minefield of current criticisms of p values

* material on non-Bayesian prior p values and posterior p values not previously published

統計學家們已經開發出新的實證貝葉斯方法，結合了傳統貝葉斯統計和頻率論統計的優點，以滿足同時測試數百或數千個基因組學假設的需求。用於估計局部虛假發現率的技術可以在不需要主觀先驗分佈的情況下，分配基因表達、基因關聯等的概率。本書將這些方法介紹給科學家，同時保持數學內容的初級水平。讀者將學習局部虛假發現率背後的基本概念，使他們能夠分析自己的基因組學數據並對已發表的基因組學研究進行批判性評估。

主要特點：
- 使用骰子遊戲和練習，包括使用互動軟件的練習，以在課堂上教授概念。
- 重點介紹基因表達和基因關聯數據，並簡要介紹代謝組學數據和蛋白質組學數據。
- 逐步介紹所需的數學方程式。
- 如何在多重假設檢驗的不同方法之間進行選擇。
- 如何將基因組學假設檢驗軟件的輸出轉換為局部虛假發現率的估計值。
- 引導讀者穿越當前對p值的批評的雷區。
- 關於非貝葉斯先驗p值和後驗p值的材料，此前未發表。

David R. Bickel is an Associate Professor in the Department of Biochemistry, Microbiology and Immunology of the University of Ottawa and a Core Member of the Ottawa Institute of Systems Biology. Since 2011, he has been teaching classes focused on the statistical analysis of genomics data. While working as a biostatistician in academia and industry, he has published new statistical methods for analyzing genomics data in leading statistics and bioinformatics journals. He is also investigating the foundations of statistical inference. For recent activity, see davidbickel.com or follow him at @DavidRBickel (Twitter).

David R. Bickel是渥太華大學生物化學、微生物學和免疫學系的副教授，也是渥太華系統生物學研究所的核心成員。自2011年以來，他一直在教授關於基因組學數據統計分析的課程。在學術界和工業界擔任生物統計學家期間，他在領先的統計學和生物信息學期刊上發表了用於分析基因組學數據的新統計方法。他還在研究統計推斷的基礎。有關最近的活動，請參閱davidbickel.com或在Twitter上關注他的@DavidRBickel帳號。