---
title: "Creating a Volcano Plot"
format: html
---

## Introduction


Here we will create a volcano plot from differential expression results. 

A volcano plot is a type of scatter plot commonly used in RNA-Seq analysis to examine genes that may demonstrate biological significance. Log-fold change in expression is plotted on the x-axis and statistical significance is plotted on the y-axis. Learn more about Volcano plots [here](https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-volcanoplot/tutorial.html){.external target="_blank"}.  


Note: In the following plot, labels are Ensembl IDs. For a more useful figure, consider adding an annotation step.

## Create a Volcano Plot from DESeq2 differential expression results  

### Load the libraries
```{r}
library(EnhancedVolcano)
library(dplyr)
```


### Load the data from command line arguments  
The data were filtered to remove adjusted p-values that were NA; these were genes excluded by `DESeq2` as a part of independent filtering.  
```{r}

data<-read.csv("deseq2_DEGs.csv",row.names=1) %>% filter(!is.na(padj))
```
  
### Plot 
  
Create label subsets for plotting.   

```{r}
labs<-head(row.names(data),5)

```

Figure 1 allows us to identify which genes are statistically significant with large fold changes.  
```{r}
EnhancedVolcano(data,
                title = "Enhanced Volcano with Airways",
                lab = rownames(data),
                selectLab=labs,
                labSize=3,
                drawConnectors = TRUE,
                x = 'log2FoldChange',
                y = 'padj')   

```