A Minimal Book Example
Preface
1
Introduction
1.1
Introduction
1.2
Installation
1.2.1
R package
1.2.2
Docker image
1.3
Get started
1.3.1
Launch the QRAP application in R or Rstudio
1.3.2
Launch the QRAP application by docker image in shell
1.3.3
Access the interactive analysis interface
2
Data input and pre-processing
2.1
Data input
2.1.1
Upload local file
2.1.2
Pull down GEO datasets
2.2
Data pre-filtering
2.3
Design table & formula
2.4
Batch effect correction
3
Data quality exploring
3.1
Principal component analysis (PCA)
3.2
Hierarchical clustering
3.3
Sample-to-sample distance
3.4
Sample correlation coefficient
4
Differential expression analysis
4.1
Extract DEGs
4.1.1
Wald significance tests
4.1.2
Likelihood ratio test
4.2
Visualize DEGs
5
DEG expression pattern detection
5.1
One-way time coures experiment
5.1.1
pre-processing and preparation
5.1.2
Detect DEG expression pattern
5.1.3
Visualize DEG expression pattern
5.2
Two-way time coures experiment
5.2.1
pre-processing and preparation
5.2.2
Detect DEG expression pattern
5.2.3
Visualize DEG expression pattern
6
weighted correlation network analysis
6.1
Data preparation
6.2
Soft threshold detection
6.3
Gene module detection
6.4
Module-Traits relationship
6.5
MM vs. GS scatterplot
6.6
Module gene expression visualization
7
functional enrichment analysis
7.1
Gprofiler API
7.2
ClusterProfiler-ORA
7.3
ClusterProfiler-GSEA
8
Gene regulatory network
8.1
KEGG Pathview
8.2
PPI network
8.3
GENIE3 inffered network
9
Summary of genes and functions
9.1
summarize genes
9.2
summarize functions
References
Published with bookdown
QRAP: an R package and Shiny app for interactive RNA sequencing data analysis
Chapter 6
weighted correlation network analysis
We have finished a nice book.
6.1
Data preparation
6.2
Soft threshold detection
6.3
Gene module detection
6.4
Module-Traits relationship
6.5
MM vs. GS scatterplot
6.6
Module gene expression visualization