---
title: "trackViewer Vignette: dandelionPlot"
author: "Jianhong Ou, Lihua Julie Zhu"
date: "`r BiocStyle::doc_date()`"
package: "`r BiocStyle::pkg_ver('trackViewer')`"
abstract: >
  Visualize high dense methylation or mutation data
  along with annotation as track layers via Dandelion Plot.
vignette: >
  %\VignetteIndexEntry{trackViewer Vignette: dandelionPlot}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output:
  html_document:
    theme: simplex
    toc: true
    toc_float: true
    toc_depth: 4
    fig_caption: true
---

```{r, echo=FALSE, results="hide", warning=FALSE}
suppressPackageStartupMessages({
    library(trackViewer)
    library(rtracklayer)
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    library(org.Hs.eg.db)
    library(VariantAnnotation)
  library(httr)
})
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```


# Dandelion Plot
Sometimes, there are as many as hundreds of SNPs invoved in one gene. Dandelion plot can be used to depict such dense SNPs. Please note that the height of the dandelion indicates the density of the SNPs.
```{r fig.width=4.5,fig.height=3}
library(trackViewer)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(rtracklayer)
methy <- import(system.file("extdata", "methy.bed", package="trackViewer"), "BED")
gr <- GRanges("chr22", IRanges(50968014, 50970514, names="TYMP"))
trs <- geneModelFromTxdb(TxDb.Hsapiens.UCSC.hg19.knownGene,
                         org.Hs.eg.db,
                         gr=gr)
features <- c(range(trs[[1]]$dat), range(trs[[5]]$dat))
names(features) <- c(trs[[1]]$name, trs[[5]]$name)
features$fill <- c("lightblue", "mistyrose")
features$height <- c(.02, .04)
dandelion.plot(methy, features, ranges=gr, type="pin")
```

## Change the type of Dandelion plot
There are one more type for dandelion plot, i.e., type "fan". The area of the fan indicates the percentage of methylation or rate of mutation.
```{r fig.width=4.5,fig.height=3}
methy$color <- 3
methy$border <- "gray"
## Score info is required and the score must be a number in [0, 1]
m <- max(methy$score)
methy$score <- methy$score/m
dandelion.plot(methy, features, ranges=gr, type="fan")
```
```{r fig.width=4.5,fig.height=3.5}
methy$color <- rep(list(c(3, 5)), length(methy))
methy$score2 <- (max(methy$score) - methy$score)/m
legends <- list(list(labels=c("s1", "s2"), fill=c(3, 5)))
dandelion.plot(methy, features, ranges=gr, type="pie", legend=legends)
```

## Change the number of dandelions
```{r fig.width=4.5,fig.height=4}
## Less dandelions
dandelion.plot(methy, features, ranges=gr, type="circle", maxgaps=1/10)
```
```{r fig.width=4.5,fig.height=3}
## More dandelions
dandelion.plot(methy, features, ranges=gr, type="circle", maxgaps=1/100)
```

Users can also specify the maximum distance between neighboring dandelions by setting the maxgaps as a GRanges object.

```{r fig.width=4,fig.height=3}
maxgaps <- tile(gr, n = 10)[[1]]
dandelion.plot(methy, features, ranges=gr, type="circle", maxgaps=maxgaps)
```

## Add y-axis (yaxis)
Set yaxis to TRUE to add y-axis, and set `heightMethod`=`mean` to use the mean score as the height.
```{r fig.width=4.5,fig.height=3}
dandelion.plot(methy, features, ranges=gr, type="pie", 
               maxgaps=1/100, yaxis = TRUE, heightMethod = mean,
               ylab='mean of methy scores')
```
```{r fig.width=4.5,fig.height=3}
yaxis = c(0, 0.5, 1)
dandelion.plot(methy, features, ranges=gr, type="pie", 
               maxgaps=1/100, yaxis = yaxis, heightMethod = mean,
               ylab='mean of methy scores')
```

```{r fig.width=4.5,fig.height=3}
# subset the data to test when all values smaller than 1
methy <- methy[methy$score<1]
dandelion.plot(methy, features, ranges=gr, type="pie", 
               maxgaps=1/100, yaxis = TRUE, heightMethod = mean,
               ylab='mean of methy scores')
```

# Session Info
```{r sessionInfo, results='asis'}
sessionInfo()
```

