Acknowledgments
The authors would like to thank Alex Nones for proofreading the manuscript during its various stages. Also, thanks to Karl Broman for contributing the “Plots to Avoid” section and to Stephanie Hicks for designing some of the exercises. Finally, thanks to John Kimmel and three anonymous referees for excellent feedback and constructive criticism of the book.
This book was conceived during the teaching of several HarvardX courses, coordinated by Heather Sternshein. We are also grateful to our TAs, Idan Ginsburg and Stephanie Chan, and all the students whose questions and comments helped us improve the book. The courses were partially funded by NIH grant R25GM114818. We are very grateful to the National Institute of Health for its support.
A special thanks goes to all those that edited the book via GitHub pull requests: vjcitn, yeredh, ste-fan, molx, kern3020, josemrecio, hcorrada, neerajt, massie, jmgore75, molecules, lzamparo, eronisko, obicke, knbknb, and devrajoh.
Cover image credit: this photograph is La Mina Falls, El Yunque National Forest, Puerto Rico, taken by Ron Kroetz https://www.flickr.com/photos/ronkroetz/14779273923 Attribution-NoDerivs 2.0 Generic (CC BY-ND 2.0)
{mainmatter}
简介
在二十世纪的下半叶，数字技术的革新带来了测量技术的革新，从而也大大的改变了科研领域。在生命科学领域，数据分析已经是科研项目中的家常便饭了。尤其是基因组学，新的技术使得我们们能够观察到我们过去没有办法观察到的特定的。历史上显微镜的发明使得我们能够鉴定新的微生物，而近些年的新技术技术引领我们发现更多生命科学的奥秘，而这些发现不亚于之前显微镜发明带给我们的帮助。这些技术中的代表就是芯片技术和下一代测序技术。
这些技术使得一些传统的依赖简单的数据分析的科研领域发生了翻天覆地的变化。例如，过去科学家如果对某一个基因感兴趣。，可能会测量其表达量。今天，利用新的技术我们可以一次性测量超过2万个人类基因的表达量。以前我们的研究是受假设驱动的，技术进步使得科研领域从
由于越来越多的研究开始依赖于上面我们提到的数据，许多生命科学领域的科研工作者开始转变成数据分析专家。这本书就是由这样的一批科研工作者写的。如果你自己分析自己的数据，那么你很可能会计算P值，应用Bonferroni矫正，进行主成分分析，产生一个热图等等。如果你发现你并不是十分理解这些技术，或者你不确定你应用这些技术时是否恰当，那么这本书就是为你写的。
尽管这本书大部分是着重于高级的统计概念，但是我们是从最基本的知识开始的，从而确保所有的读者能够有一个扎实的数据分析的统计基础。我们发现许多基础统计课程的方式是读者很难讲概念性的东西和真正的数据分析联系起来。我们的方法会确保你能学习到实践和理论的结合。所以，前两章的内容（推断与探索性数据分析）适用于本科阶段的统计或者数据科学的入门课程。接着，统计的难度就会慢慢的增加。
尽管本书的读者通常会有硕士或者博士学位，我们会尽量将数学的内容保持在本科入门水平。这本书中你不需要用到微积分。然而，我们会介绍和使用线性代数，而线性代数通常被认为比微积分难的一门学科。但是我们会在数据分析的背景下来理解线性代数，我们相信在这种情况下，你可以在不懂微积分的情况下理解基础的线性代数知识。比较难的部分可能是你需要适应符号和公式。——–
这本书包含什么内容？
这本书包含以数据为主的生命科学研究中的一些统计概念和数据分析的技巧。我们从简单的P值计算开始，一步步到分析高通量数据的高级内容。
我们首先会介绍统计和生命科学领域中的一个最重要的内容：统计推断。统计推断是利用概率来掌握数据的群体特征。一个经典的例子是判断两组数据的均值是否有差异。其他的内容包括t检验，置信区间，联想测验（association test），蒙特卡洛方法，置换检验和统计效力。我们会使用经典数学理论带给我们的中心极限定理和当代计算发展……………
We then cover the concepts of distance and dimension reduction. We will introduce the mathematical definition of distance and use this to motivate the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling. Once we learn this, we will be ready to cover hierarchical and k-means clustering. We will follow this with a basic introduction to machine learning.
We end by learning about batch effects and how component and factor analysis are used to deal with this challenge. In particular, we will examine confounding, show examples of batch effects, make the connection to factor analysis, and describe surrogate variable analysis.
这本书有什么不同？
While statistics textbooks focus on mathematics, this book focuses on using a computer to perform data analysis. This book follows the approach of Stat Labs, by Deborah Nolan and Terry Speed. Instead of explaining the mathematics and theory, and then showing examples, we start by stating a practical data-related challenge. This book also includes the computer code that provides a solution to the problem and helps illustrate the concepts behind the solution. By running the code yourself, and seeing data generation and analysis happen live, you will get a better intuition for the concepts, the mathematics, and the theory.
通常统计相关的课本重点放在数学公式上，这本书重点是利用计算机来进行数据分析。这本书所遵循的方法与Deborah Nolan和Terry Speed所著的Stat Labs是一样的。通常统计课本是先解释数学和理论知识，然后展示例子，我们是从一个具体的实践中会遇到的数据分析问题开始。这本书也包含解决这些问题的具体的R代码，这些代码可以帮助读者理解解决方法。
We focus on the practical challenges faced by data analysts in the life sciences and introduce mathematics as a tool that can help us achieve scientific goals. Furthermore, throughout the book we show the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts we explain. All sections of this book are reproducible as they were made using R markdown documents that include R code used to produce the figures, tables and results shown in the book. In order to distinguish it, the code is shown in the following font:
x <- 2
y <- 3
print(x+y) and the results in different colors, preceded by two hash characters (##):
## [1] 5We will provide links that will give you access to the raw R markdown code so you can easily follow along with the book by programming in R.
At the beginning of each chapter you will see the sentence:
The R markdown document for this section is available here.
The word “here” will be a hyperlink to the R markdown file. The best way to read this book is with a computer in front of you, scrolling through that file, and running the R code that produces the results included in the book section you are reading.