---
title: "TP SingleCell GSEA"
output: html_document
date: "2023-11-08"
---

# Load libraries

```{r load_libraries, echo=TRUE, eval=FALSE}
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 = "UpSetR")          # Nice venn-like graphs
base::library(package = "EnhancedVolcano") # Draw Volcano plot
```

# Load RDS object

```{r load_seurat_object, echo=TRUE, eval=TRUE}
sobj <- base::readRDS(
  file = "/shared/projects/2325_ebaii/SingleCell/GSEA/Scaled_Normalized_Seurat_Object.RDS",
)
```

# Your time to work!

## Get Gene-Sets

Use the function `getGeneSets` from the `escape` package.

Remember:

1. we work on a mouse dataset
2. we use the library `MH`

```{r gene_sets, echo=TRUE, eval=TRUE}
# Write down your code here
```

## Run Escape

Use the function `enrichIt` of the package `escape`.

Remember:

1. We use the gene set we justcreated
2. We use the Seurat object stored in the variable `sobj`
3. If we use more CPU (`cores`) the analysis is faster!

```{r gsea, echo=TRUE, eval=TRUE}
# Write down your code here
```

## Session info

Use the function `sessionINfo()` to keep a track of your package version!

Remember:

1. I can't remember the package name... Can you find it?

```{r session_info, echo=TRUE, eval=TRUE}
# Write down your code here
```