12_Gene_set_testing.Solutions.Rmd

---
title: "Introduction to Bulk RNAseq data analysis"
subtitle: "Gene Set Testing for RNA-seq - Solutions"
output:
  html_document:
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes  
layout: page
always_allow_html: true
---

```{r setup, include=FALSE}
library(msigdbr)
library(clusterProfiler)
library(pathview)
library(org.Mm.eg.db)
library(tidyverse)
knitr::opts_knit$set(cache=TRUE)
options(bitmapType='cairo')
knitr::opts_chunk$set(dev = c("png"))
```

```{r prepareORAData, include=FALSE}
shrink.d11 <- readRDS("RObjects/Shrunk_Results.d11.rds")

# Kegg data
sigGenes <-  shrink.d11 %>% 
    drop_na(Entrez, FDR) %>% 
    filter(FDR < 0.01 & abs(logFC) > 1) %>% 
    pull(Entrez)
kk <- enrichKEGG(gene = sigGenes, organism = 'mmu')
```

## Exercise 1 - pathview

> 1. Use `pathview` to export a figure for "mmu04659", but this time only
> use genes that are statistically significant at FDR < 0.01

```{r solution1}
logFC <- shrink.d11 %>% 
  drop_na(FDR, Entrez) %>% 
  filter(FDR < 0.01) %>% 
  dplyr::select(Entrez, logFC) %>% 
  deframe()

pathview(gene.data = logFC, 
         pathway.id = "mmu04659", 
         species = "mmu", 
         limit = list(gene=5, cpd=1))
```

mmu04659.pathview.png:

![mmu04659 - Th17 cell differentiation](images/mmu04659_pathview.png)

## Exercise 2 - GO term enrichment analysis

> `clusterProfiler` can also perform over-representation analysis on GO terms.
> using the commmand `enrichGO`. Look at the help page for the command 
> `enrichGO` (`?enrichGO`) and have a look at the instructions in the 
> [clusterProfiler book](http://yulab-smu.top/clusterProfiler-book/chapter5.html#go-over-representation-test).
>
> 1. Run the over-representation analysis for GO terms 
>     - Use genes that have an adjusted p-value (FDR) of less than 0.01 and
> an absolute fold change greater than 2.    
>     - For this analysis you can use Ensembl IDs rather then Entrez
>     - You'll need to provide the background (`universe`) genes, this should be
> all the genes in our analysis.
>     - The mouse database package is called `org.Mm.eg.db`. You'll need to load
>  it using `library` before running the analysis.    
>     - As we are using Ensembl IDs, you'll need to set the `keyType` 
> parameter in the `enrichGO` command to indicate this.
>     - Only test terms in the "Biological Processes" ontology 
> 2. Use the `dotplot` function to visualise the results.

```{r solution2}

sigGenes <-  shrink.d11 %>% 
    drop_na(FDR) %>% 
    filter(FDR < 0.01 & abs(logFC) > 1) %>% 
    pull(GeneID)

universe <- shrink.d11$GeneID

ego <- enrichGO(gene          = sigGenes, 
                universe      = universe,
                OrgDb         = org.Mm.eg.db,
                keyType       = "ENSEMBL",
                ont           = "BP",
                pvalueCutoff  = 0.01,
                readable      = TRUE)

dotplot(ego,
        font.size = 8,
        )
```

```{r}
barplot(ego, 
        drop = TRUE, 
        showCategory = 10,
        label_format = 20,
        title = "GO Biological Pathways",
        font.size = 8)
```


## Exercise 3 - GSEA

> Another common way to rank the genes is to order by pvalue, but also, sorting
> so that upregulated genes are at the start and downregulated at the end - 
> you can do this combining the sign of the fold change and the pvalue. 
> 
> 1. Rank the genes by statisical significance - you will need to create
> a new ranking value using `-log10({p value}) * sign({Fold Change})`  
> 2. Run `fgsea` using the new ranked genes and the H pathways  
> 3. Conduct the same analysis for the d33 vs control contrast.

### Exercise 3 - d11 new rank

```{r solution3_GSEA_1}
# 1. Rank the genes by statistical significance - you will need to create
# a new ranking value using `-log10({p value}) * sign({Fold Change})`  

# obtain the H(allmarks) catalog for mouse:
m_H_t2g <- msigdbr(species = "Mus musculus", category = "H") %>% 
  dplyr::select(gs_name, entrez_gene, gene_symbol)

# rank genes
rankedGenes.e1 <- shrink.d11 %>%
  drop_na(Entrez, pvalue, logFC) %>%
  # rank genes by strength of significance,
  # keeping the direction of the fold change 
  mutate(rank = -log10(pvalue) * sign(logFC)) %>%
  # sort genes by decreasing rank.
  arrange(-rank) %>%
  # keep ranks and Entrez IDs
  pull(rank,Entrez)

# conduct analysis:
gseaRes.e1 <- GSEA(rankedGenes.e1,
                TERM2GENE = m_H_t2g[,c("gs_name", "entrez_gene")],
                #pvalueCutoff = 0.05,
                pvalueCutoff = 1.00, # to retrieve whole output
                minGSSize = 15,
                maxGSSize = 500)
```

```{r}
# have function to format in scientific notation
format.e1 <- function(x) (sprintf("%.1e", x))
# format table:
gseaRes.e1 %>%
  # sort in decreasing order of absolute NES
  arrange(desc(abs(NES))) %>%
  # only keep the 10 entries with the lowest p.adjust
  top_n(10, -p.adjust) %>%
  # remove columns 'core_enrichment' and 'Description'
  dplyr::select(-core_enrichment) %>%
  dplyr::select(-Description) %>%
  # convert to data.frame
  data.frame() %>%
  # remove row names
  remove_rownames() %>%
  # format score
  mutate(NES=formatC(NES, digits = 3)) %>%
  mutate(ES=formatC(enrichmentScore, digits = 3)) %>%
  relocate(ES, .before=NES) %>%
  dplyr::select(-enrichmentScore) %>%
  # format p-values
  modify_at(
    c("pvalue", "p.adjust", "qvalues"),
    format.e1
  ) %>%
  # display
  DT::datatable(options = list(dom = 't'))
```

### Exercise 3 - d33

With d33 and H catalog:

```{r solution3_GSEA_3}

# read d33 data in:
shrink.d33 <- readRDS("RObjects/Shrunk_Results.d33.rds")

# get mouse H(allmarks) catalog
m_H_t2g <- msigdbr(species = "Mus musculus", category = "H") %>% 
  dplyr::select(gs_name, entrez_gene, gene_symbol)

# rank genes
rankedGenes.e3 <- shrink.d33 %>%
  drop_na(Entrez, pvalue, logFC) %>%
  mutate(rank = -log10(pvalue) * sign(logFC)) %>%
  arrange(-rank) %>%
  pull(rank,Entrez)

# perform analysis
gseaRes.e3 <- GSEA(rankedGenes.e3,
                TERM2GENE = m_H_t2g[,c("gs_name", "entrez_gene")],
                #pvalueCutoff = 0.05,
                pvalueCutoff = 1.00, # to retrieve whole output
                minGSSize = 15,
                maxGSSize = 500)
```

Check outcome:

```{r}
gseaRes.e3 %>%
  arrange(desc(abs(NES))) %>%
  top_n(10, -p.adjust) %>%
  dplyr::select(-core_enrichment) %>%
  dplyr::select(-Description) %>%
  data.frame() %>%
  remove_rownames() %>%
  # format score
  mutate(NES=formatC(NES, digits = 3)) %>%
  mutate(ES=formatC(enrichmentScore, digits = 3)) %>%
  relocate(ES, .before=NES) %>%
  dplyr::select(-enrichmentScore) %>%
  # format p-values
  modify_at(
    c("pvalue", "p.adjust", "qvalues"),
    format.e1
  ) %>%
  DT::datatable(options = list(dom = 't'))
```