In this presentation, there will be screen captures for you to follow the lesson. There will also be every single R command lines. Do not take care of the command lines if you find them too challenging. Our goal here, is to understand the main mechanism of Differential Expression Analysis. R is just a tool.
Below are the libraries we need to perform this whole session:
base::library(package = "BiocParallel") # Optionally multithread some steps
base::library(package = "DT") # Display nice table in HTML
base::library(package = "ggplot2") # Draw graphs and plots
base::library(package = "ggpubr") # Draw nicer graphs
base::library(package = "rstatix") # Base R statistics
base::library(package = "knitr") # Build this presentation
base::library(package = "dplyr") # Handle big tables
base::library(package = "Seurat") # Handle SingleCell analyses
base::library(package = "SeuratObject") # Handle SingleCell objects
base::library(package = "SingleCellExperiment") # Handle SingleCell file formats
base::library(package = "escape") # Perform exploratory enrichment analysis
base::library(package = "clusterProfiler") # Perform GSEA analysis
base::library(package = "dittoSeq") # Draw nice plots based on Seurat
base::library(package = "org.Mm.eg.db") # Mouse genome annotation
base::library(package = "Cairo") # Graphs library
base::library(package = "pathview") # For the whole pathway graph
First, we load Seurat object:
Then we launch enrichment exploration on all counts:
Up to now, we have:
We would like to identify the functions of genes among several clusters, or differentially expressed genes.
At the end of this session you will know: