生物信息R数据分析
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Acknowledgments
简介
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1
入门介绍
1.1
安装R
1.2
R基础知识
1.3
Installing Packages
1.4
Importing Data into R
1.4.1
Getting Started Exercises
1.5
Brief Introduction to
dplyr
1.5.1
dplyr
exercises
1.6
Mathematical Notation
2
统计推断
2.1
简介
2.1.1
认识数据
2.2
随机变量
2.3
零假设
2.4
Distributions
2.5
Probability Distribution
2.6
Normal Distribution
2.7
Populations, Samples and Estimates
2.7.1
Population, Samples, and Estimates Exercises
2.8
Central Limit Theorem and t-distribution
2.9
Central Limit Theorem in Practice
2.10
t-tests in Practice
2.11
The t-distribution in Practice
2.12
Confidence Intervals
2.13
Power Calculations
2.14
Monte Carlo Simulation
2.15
Parametric Simulations for the Observations
2.16
Permutation Tests
2.17
Association Tests
3
探索性数据分析
3.1
QQ图
3.2
箱线图
3.3
散点图和相关性
3.4
数据分层
3.5
二维正态分布
3.6
这些图需要避免使用！
3.7
Misunderstanding Correlation (Advanced)
3.8
Robust Summaries
3.9
非参检验方法之Wilcoxon Rank Sum Test
4
矩阵代数
4.1
用实例来讲话
4.2
Matrix Notation
4.3
Solving Systems of Equations
4.4
Vectors, Matrices, and Scalars
4.5
Matrix Operations
4.6
Examples
5
Linear Models
5.1
The Design Matrix
5.2
The Mathematics Behind lm()
5.3
Standard Errors
5.4
Interactions and Contrasts
5.5
Linear Model with Interactions
5.6
Analysis of Variance
5.7
Collinearity
5.8
Rank
5.9
Removing Confounding
5.10
The QR Factorization (Advanced)
5.11
Going Further
6
Inference for High Dimensional Data
6.1
Introduction
6.2
Inference in Practice
6.3
Procedures
6.4
Error Rates
6.5
The Bonferroni Correction
6.6
False Discovery Rate
6.7
Direct Approach to FDR and q-values (Advanced)
6.8
Basic Exploratory Data Analysis
7
统计模型
7.1
二项分布 (The Binomial Distribution)
7.2
泊松分布 (The Poisson Distribution)
7.3
最大似然估计
7.4
连续变量的分布
7.5
贝叶斯统计
7.6
Hierarchical Models
8
距离和维度降低
8.1
简介
8.2
Euclidean Distance
8.3
高维数据的距离
8.4
Distance exercises
8.5
Dimension Reduction Motivation
8.6
Singular Value Decomposition
8.7
Projections
8.8
Rotations
8.9
Multi-Dimensional Scaling Plots
8.10
MDS exercises
8.11
Principal Component Analysis
9
Basic Machine Learning
9.1
Clustering
9.2
Conditional Probabilities and Expectations
9.3
Smoothing
9.4
Bin Smoothing
9.5
Loess
9.6
Class Prediction
9.7
Cross-validation
10
Batch Effects
10.1
Confounding
10.2
Confounding: High-Throughput Example
10.3
Discovering Batch Effects with EDA
10.4
Gene Expression Data
10.5
Motivation for Statistical Approaches
10.6
Adjusting for Batch Effects with Linear Models
10.7
Factor Analysis
10.8
Modeling Batch Effects with Factor Analysis
参考文献
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生物信息R数据分析
参考文献