EdgeR: Differential Expression Analysis Of Digital Gene Data User's Guide Edge RUsers
User Manual:
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Page Count: 110 [warning: Documents this large are best viewed by clicking the View PDF Link!]
- 1 Introduction
- 2 Overview of capabilities
- 2.1 Terminology
- 2.2 Aligning reads to a genome
- 2.3 Producing a table of read counts
- 2.4 Reading the counts from a file
- 2.5 The DGEList data class
- 2.6 Filtering
- 2.7 Normalization
- 2.8 Negative binomial models
- 2.9 Pairwise comparisons between two or more groups (classic)
- 2.10 More complex experiments (glm functionality)
- 2.11 What to do if you have no replicates
- 2.12 Differential expression above a fold-change threshold
- 2.13 Gene ontology (GO) and pathway analysis
- 2.14 Gene set testing
- 2.15 Clustering, heatmaps etc
- 2.16 Alternative splicing
- 2.17 CRISPR-Cas9 and shRNA-seq screen analysis
- 2.18 Bisulfite sequencing and differential methylation analysis
- 3 Specific experimental designs
- 4 Case studies
- 4.1 RNA-Seq of oral carcinomas vs matched normal tissue
- 4.2 RNA-Seq of pathogen inoculated arabidopsis with batch effects
- 4.3 Profiles of Yoruba HapMap individuals
- 4.4 RNA-Seq profiles of mouse mammary gland
- 4.4.1 Introduction
- 4.4.2 Read alignment and processing
- 4.4.3 Count loading and annotation
- 4.4.4 Filtering and normalization
- 4.4.5 Data exploration
- 4.4.6 The design matrix
- 4.4.7 Estimating the dispersion
- 4.4.8 Differential expression
- 4.4.9 ANOVA-like testing
- 4.4.10 Gene ontology analysis
- 4.4.11 Gene set testing
- 4.4.12 Setup
- 4.5 Differential splicing after Pasilla knockdown
- 4.5.1 Introduction
- 4.5.2 RNA-Seq samples
- 4.5.3 Read alignment and processing
- 4.5.4 Count loading and annotation
- 4.5.5 Filtering and normalization
- 4.5.6 Data exploration
- 4.5.7 The design matrix
- 4.5.8 Estimating the dispersion
- 4.5.9 Differential expression
- 4.5.10 Alternative splicing
- 4.5.11 Setup
- 4.5.12 Acknowledgements
- 4.6 CRISPR-Cas9 knockout screen analysis
- 4.7 Bisulfite sequencing of mouse oocytes
- 4.7.1 Introduction
- 4.7.2 Reading in the data
- 4.7.3 Filtering and normalization
- 4.7.4 Data exploration
- 4.7.5 The design matrix
- 4.7.6 Estimating the dispersion
- 4.7.7 Differential methylation analysis at CpG loci
- 4.7.8 Summarizing counts in promoter regions
- 4.7.9 Differential methylation in gene promoters
- 4.7.10 Setup