推荐一个R & Bioconductor的使用手册网站

这个网站上包含了Bioconductor各种分析的R脚本和方法，包括：KEGG，pathway分析。如下所示：

网站链接：http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual

Contents

1 R Basics

1.1 Introduction

1.2 Finding Help

1.3 Basics on Functions and Packages

1.4 System commands under Linux

1.5 Reading and Writing Data from/to Files

1.6 R Objects

1.6.1 Data and Object Types

1.6.2 General Subsetting Rules

1.6.3 Basic Operators and Calculations

1.6.4 Regular expressions

1.6.5 Vectors

1.6.6 Factors

1.6.7 Matrices and Arrays

1.6.8 Data Frames

1.6.9 Lists

1.7 Missing Values

1.8 Some Great R Functions

1.9 Graphical Procedures

1.9.1 Introduction to R Graphics

1.9.2 Scatter Plots

1.9.3 Line Plots

1.9.4 Bar Plots

1.9.5 Pie Charts

1.9.6 Heatmaps

1.9.7 Venn Diagrams

1.9.8 Intersect Plots

1.9.9 Histograms

1.9.10 Density Plots

1.9.11 Box Plots

1.9.12 ROC Curves

1.9.13 Feature Maps for DNA/Protein Sequences

1.9.14 Miscellaeneous Plotting Utilities

1.9.15 Arranging Plots

1.9.16 Customize X-Y Axes

1.9.17 Color Selection Utilities

1.9.18 Adding Text

1.9.19 Interactive Functions

1.9.20 Saving Graphics to Files

1.9.21 Importing Graphics into R

1.9.22 Graphics Exercises

1.10 Writing Your Own Functions

1.11 R Web Applications

1.12 R Basics Exercises

2 HT Sequence Analysis with R and Bioconductor

3 Programming in R

4 Bioconductor

4.1 Introduction

4.2 Finding Help

4.3 Affy Packages

4.3.1 Affy

4.3.2 Visualization and quality controls

4.3.3 Simpleaffy

4.4 Analysis of Differentially Expressed Genes

4.4.1 Limma

4.4.1.1 Limma: Dual Color Arrays

4.4.1.2 Limma: Affymetrix Arrays

4.4.2 RankProd

4.5 Additional Dual Color Array Packages

4.5.1 Marray

4.6 Chromosome maps

4.7 Gene Ontologies

4.7.1 General

4.7.2 GOHyperGAll  

4.7.3 GSEA

4.7.4 GOTools and goCluster

4.8 KEGG Pathway Analysis

4.9 Motif Identification in Promoter Regions

4.10 Phylogenetic Analysis

4.11 Cheminformatics in R

4.12 Protein Structure Analysis

4.13 MS Data Analysis

4.14 Genome-Wide Association Studies (GWAS)

4.15 BioConductor Exercises

5 Clustering and Data Mining in R

5.1 Introduction

5.2 Data Preprocessing

5.3 Hierarchical Clustering (HC)

5.4 Bootstrap Analysis in Hierarchical Clustering 

5.5 QT Clustering 

5.6 K-Means & PAM 

5.7 Fuzzy Clustering 

5.8 Self-Organizing Map (SOM) 

5.9 Principal Component Analysis (PCA) 

5.10 Multidimensional Scaling (MDS) 

5.11 Bicluster Analysis 

5.12 Network Analysis

5.13 Support Vector Machines (SVM) 

5.14 Similarity Measures for Clustering Results 

5.15 Clustering Exercises

6 Administration

感谢 @玉米青蛙 同学的推荐