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Manuals Under Construction!
Overview
Introduction
Class teaching
Introduction to R
1. Overview
2. R Package Repositories
3. Installation of R and Add-on Packages
4. Getting Around
5. Basic Syntax
6. Data Types
7. Data objects
8. Important Utilities
9. Operators and Calculations
10. Reading and Writing External Data
11. Useful R Functions
12. SQLite Databases
13. Graphics in R
14. Analysis Routine
15. R Markdown
16. Session Info
17. References
Graphics and Data Visualization in R
1. Overview
2. Base Graphics
3. Grid Graphics
4. lattice Graphics
5. ggplot2 Graphics
6. Specialty Graphics
7. Genome Graphics
8. References
Cluster Analysis in R
1. Introduction
2. Data Preprocessing
3. Clustering Algorithms
4. Clustering Exercises
5. Version Information
6. References
Programming in R
1. Overview
2. Control Structures
3. Loops
4. Functions
5. Useful Utilities
6. Running R Scripts
7. Building R Packages
8. Programming Exercises
9. Homework 5
10. Session Info
11. References
NGS Analysis Basics
1. Overview
2. Package Requirements
3. Strings in R Base
4. Sequences in Bioconductor
5. NGS Sequences
6. Range Operations
7. Transcript Ranges
8. Homework 6
9. Session Info
10. References
Designing and Running NGS Workflows
1. Introduction
2. Getting Started
3. Workflow overview
4. Workflow templates
5. Version information
6. References
RNA-Seq Workflow Template
1. Introduction
2. Sample definitions and environment settings
3. Read preprocessing
4. Alignments
5. Read quantification per annotation range
6. Analysis of differentially expressed genes with edgeR
7. GO term enrichment analysis of DEGs
8. Clustering and heat maps
9. Version Information
10. Funding
11. References
ChIP-Seq Workflow Template
1. Introduction
2. Load workflow environment
3. Read preprocessing
4. Alignments
5. Utilities for coverage data
6. Peak calling with MACS2
7. Annotate peaks with genomic context
8. Count reads overlapping peaks
9. Differential binding analysis
10. GO term enrichment analysis
11. Motif analysis
12. Version Information
13. Funding
14. References
VAR-Seq Workflow Template
1. Introduction
2. Load workflow environment
3. Read preprocessing
4. Alignments
5. Variant calling
6. Filter variants
7. Annotate filtered variants
8. Combine annotation results among samples
9. Summary statistics of variants
10. Venn diagram of variants
11. Plot variants programmatically
12. Version Information
13. Funding
14. References
Internal notes
Administration of site
References
Hathaway, R J, Bezdek, J C, Pal, N R (1996) Sequential Competitive Learning and the Fuzzy c-Means Clustering Algorithms. Neural Netw., 9: 787–796;
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