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ggplot2-based Manhattan plot

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## Introduction
The Manhattan plot is a specialized form of scatterplot to display genome-wide association studies (GWAS). The x-axis of a Manhattan plot is the genomic position, and the y-axis is usually the $-log_{10}(P\text{-}value)$ (although other sensible metric can be used as well).

There are many packages for making Manhattan plots, but most of them are not easily extensible. The package `ggplot2` has become increasingly popular among the $\textbf{R}$ and bioinformatics communities. Therefore, a package that is fully compatible with `ggplot2` and customizable with its `geom`s will facilitate the use of Manhattan plots.

Introducing `manhattan`, a `ggplot2` based package for making Manhattan plots. It takes in a `data.frame` and returns a standard `ggplot` object, upon which the user can add other `geom`s. This makes it very convenient for users to build on `manhattan` and customize their plots.

## Installation
To install `manhattan`, use the standard R package installation command.

```{r}
# install.packages('manhattan')
```

If you want the latest development version, install it using `devtools`. 
```{r}
devtools::install_github("boxiangliu/manhattan")
```

The package has been tested on Linux and Mac OSX. It has not been tested on Windows.

## Usage
### Basic Manhattan plot
To illustate its usage, let us plot the coronary artery disease GWAS based on *Deloukas et al*(2013). The original dataset provides nominal p-values. Since we want to plot the $-log_{10}(P\text{-}value)$, let us take the logarithm.

```{r,cache=TRUE}
library(manhattan)
data(cad_gwas)
cad_gwas$y=-log10(cad_gwas$pval)
head(cad_gwas)
```

The dataset contains five columns: `chrom`, `pos`, `rsid`, `pval`, and `y`. Three of them are required:

1. `chrom`
2. `pos`
3. `y`

The `chrom` and `pos` columns specify the genomic location (x-axis), and `y` specify the y-axis (duh!). The choice of the column name "y" is intentional - not every Manhanttan plot uses $-log_{10}{P\text{-}value}$ as the y-axis. After loading the data, we are ready to make a Manhattan plot. Notice that *Deloukas et al* uses hg18. To get the chromosome lengths correctly, we specify hg18 as an argument.

```{r,cache=TRUE}
manhattan(cad_gwas,build='hg18')
```

Ta-da! Our first Manhattan plot.

### Customizing colors
If black and grey are dull, we can change the color of each chromosome.

```{r,cache=TRUE}
manhattan(cad_gwas,build='hg18',color1='skyblue',color2='navyblue')
```

### Highlight and label SNPs and genes
A common task is to highlight and annotate SNPs of interest. The package `manhattan` requires color and SNP labels to be specified in the input `data.frame` as two columns: color and label. Note that only SNPs of interest have color strings, and other SNPs **must be left as `NA`**.

Let us highlight two SNPs rs602633 and rs1333045.

```{r,cache=TRUE}
cad_gwas[cad_gwas$rsid=='rs602633','color']='green'
cad_gwas[cad_gwas$rsid=='rs1333045','color']='red'
manhattan(cad_gwas,build='hg18')
```

We could also label the two SNPs. Note again that only SNPs of interest have label strings, other SNPs should be left as `NA`s. Since manhattan returns a `ggpplot` object, we could just add a `geom_text` layer.

```{r,cache=TRUE}
cad_gwas[cad_gwas$rsid=='rs602633','label']='rs602633'
cad_gwas[cad_gwas$rsid=='rs1333045','label']='rs1333045'
manhattan(cad_gwas,build='hg18')+geom_text(aes(label=label),hjust=-0.1)
```

### Adding GWAS significance line
It is worth noting that any standard `geom`s can be used with `manhattan`. For instance, let's add a line indicating genome-wide significant threshold.

```{r,cache=TRUE}
manhattan(cad_gwas,build='hg18')+geom_text(aes(label=label),hjust=-0.1)+geom_hline(yintercept=-log10(5e-8),color='red')
```

### Plotting multiple GWAS studies
We can go even further to use facets with `manhattan`. For illustration, let us pretend that we have two GWAS studies by duplicating `cad_gwas`, and plot two GWAS studies on top of each other.

```{r,cache=TRUE}
cad_gwas_2=rbind(cbind(cad_gwas,study='GWAS 1'),cbind(cad_gwas,study='GWAS 2'))
manhattan(cad_gwas_2,build='hg18')+facet_grid(study~.)
```

## Details

Behind the scene, `manhattan` is no more than a wrapper around `ggplot`, with a few tricks to transform a genomic axis to a scatterplot axis. In brief, `manhattan` transforms the chrom:pos pairs to cumulative positions. For instance, chr2:1 would be the length of chromosome 1 plus 1, chr3:1 would be chromosome 1 plus chromosome 2 plus 1, so on and so forth. Therefore, it is important to specify the genomic build (e.g. hg19) so that `manhattan` can make the correct transformation. It then positions the chromosome labels on the x-axis according to these transformations.

Again, it is important to note that `manhattan` is no more than a wrapper around `ggplot`, which makes `manhattan` highly customizable. For instance, we can change the size of the SNPs of interest by adding a `geom_point` layer.

```{r, cache=TRUE}
cad_gwas$size=ifelse(cad_gwas$rsid%in%c('rs602633','rs1333045'),5,2)
manhattan(cad_gwas,build='hg18')+geom_point(aes(color=color,size=size),show.legend=FALSE)
```

## Questions and Bugs
If you have any question or want to report a bug, please open a github issue [here](https://github.com/boxiangliu/manhattan/issues).

## Citation
If you use the `manhattan` package, please cite our paper: https://www.nature.com/articles/s41588-019-0404-0

```
Liu, Boxiang, Michael J. Gloudemans, Abhiram S. Rao, Erik Ingelsson, and Stephen B. Montgomery. "Abundant associations with gene expression complicate GWAS follow-up." Nature genetics 51, no. 5 (2019): 768-769.
```

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