Books  Data Analysis & Modelling  Data Analysis & Statistics 

Using R at the Bench: Step-By-Step Data Analytics for Biologists

Handbook / Manual

By: Martina Bremer (Author), Rebecca W Doerge (Author)

300 pages, illustrations

Cold Spring Harbor Laboratory Press

Hardback | Nov 2015 | #223157 | ISBN-13: 9781621821120
Availability: Usually dispatched within 6 days Details
NHBS Price: £37.99 $48/€45 approx

About this book

Using R at the Bench: Step-by-Step Data Analytics for Biologists is a convenient bench-side handbook for biologists, designed as a handy reference guide for elementary and intermediate statistical analyses using the free/public software package known as "R." The expectations for biologists to have a more complete understanding of statistics are growing rapidly. New technologies and new areas of science, such as microarrays, next-generation sequencing, and proteomics, have dramatically increased the need for quantitative reasoning among biologists when designing experiments and interpreting results. Even the most routine informatics tools rely on statistical assumptions and methods that need to be appreciated if the scientific results are to be correct, understood, and exploited fully.

While the original Statistics at the Bench has all examples in Excel, this new book uses the same text and examples in R. There is a new chapter that introduces the basics of R (where to download, getting people connected to it, and some basic commands and resources). There is also a new chapter that explains how to analyze Next Generation Sequencing data using R (specifically, RNA-seq). R has many functions for these analyses and Using R at the Bench: Step-by-Step Data Analytics for Biologists is an excellent resource for those biologists who want to learn R. This book is an essential handbook for working scientists providing a simple refresher for those who have forgotten what they once knew, and an overview for those wishing to use more quantitative reasoning in their research. Statistical methods, as well as guidelines for the interpretation of results, are explained using simple examples. Throughout the book, examples are accompanied by detailed R commands for easy reference.


Contents

1 Introduction
 
2 Common Pitfalls
2.1 Examples of Common Mistakes
2.2 Defining Your Question
2.3Working with and Talking to a Statistician
2.4 Exploratory versus Inferential Statistics
2.5 Different Sources of Variation
2.6 Model Assumptions are Important
2.7Statistical Software Packages
2.8 Installing and Using R and R Commander
2.8.1 Loading Data
2.8.2 Variable types
2.8.3 Handling Graphics
2.8.4 Saving Your Work
2.8.5 Getting Help
 
3 Descriptive Statistics
3.1 Definitions
3.2 Numerical Ways to Describe Data
3.2.1 Categorical Data
3.2.2 Quantitative Data
3.2.3 Determining Outliers
3.2.4 How to Choose a Descriptive Measure
3.3 Graphical Methods to Display Data
3.3.1 How to Choose the Appropriate Graphical Display for Your Data
3.4 Probability Distributions
3.4.1 The Binomial Distribution
3.4.2 The Normal Distribution
3.4.3 Assessing Normality in Your Data
3.4.4 Data Transformations
3.5 The Central Limit Theorem
3.5.1 The Central Limit Theorem for Sample Proportions
3.5.2 The Central Limit Theorem for Sample Means
3.6 Standard Deviation vs. Standard Error
3.7 Error Bars
3.8 Correlation
3.8.1 Correlation and Causation
 
4 Design of Experiments
4.1 Mathematical and Statistical Models
4.1.1 Biological Models
4.2 Describing Relationships between Variables
4.3 Choosing a Sample
4.3.1 Problems in Sampling: Bias
4.3.2 Problems in Sampling: Accuracy and Precision
4.4 Choosing a Model
4.5 Sample Size
4.6 Resampling and Replication
 
5 Confidence Intervals
5.1 Interpretation of Confidence Intervals
5.1.1 Confidence Levels
5.1.2 Precision
5.2 Computing Confidence Intervals
5.2.1 Confidence Intervals for Large Sample Mean
5.2.2 Confidence Interval for Small Sample Mean
5.2.3 Confidence Interval for Population Proportion
5.3 Sample Size Calculations
 
6 Hypothesis Testing
6.1 The Basic Principle
6.1.1 p-values
6.1.2 Errors in Hypothesis Testing
6.1.3 Power of a Test
6.1.4 Interpreting Statistical Significance
6.2 Common Hypothesis Tests
6.2.1 t-test
6.2.2 z-test
6.2.3 F-test
6.2.4 Tukey’s Test and Scheffé’s Test
6.2.5 ?2-test: Goodness-of-Fit or Test of Independence
6.2.6 Likelihood Ratio Test
6.3 Non-parametric Tests
6.3.1 Wilcoxon-Mann-Whitney Rank Sum Test
6.3.2 Fisher’s Exact Test
6.3.3 Permutation Tests
6.4 E-values
 
7 Regression and ANOVA
7.1 Regression
7.1.1 Parameter Estimation
7.1.2 Hypothesis Testing
7.1.3 Logistic Regression
7.1.4 Multiple Linear Regression
7.1.5 Model Building in Regression–Which Variables to Use?
7.1.6 Verification of Assumptions
7.1.7 Outliers in Regression
7.1.8 A Case Study
7.2 ANOVA
7.2.1 One-Way ANOVA Model
7.2.2 Two-Way ANOVA Model
7.2.3 ANOVA Assumptions
7.2.4 ANOVA Model for Microarray Data
7.3 What ANOVA and Regression models have in common
 
8 Special Topics
8.1 Classification
8.2 Clustering
8.2.1 Hierarchical Clustering
8.2.2 Partitional Clustering
8.3 Principle Component Analysis
8.4 Microarray Data Analysis
8.4.1 The Data
8.4.2 Normalization
8.4.3 Statistical Analysis
8.4.4 The ANOVA Model
8.4.5 Variance Assumptions
8.4.6 Multiple Testing Issues
8.5 The Basics of Next Generation Sequencing Analysis
8.6 Maximum Likelihood
8.7 Frequentist and Bayesian Statistics
 
Index
Index of Worked Out Examples
Index of R Commander Commands


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