---
title: "trackViewer Vignette: overview"
author: "Jianhong Ou, Lihua Julie Zhu"
date: "`r BiocStyle::doc_date()`"
package: "`r BiocStyle::pkg_ver('trackViewer')`"
abstract: >
Visualize mapped reads along with annotation as track layers for NGS dataset
such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data.
vignette: >
%\VignetteIndexEntry{trackViewer Vignette: overview}
%\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(Gviz)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(VariantAnnotation)
library(httr)
})
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```
# Introduction
There are two packages available in Bioconductor for visualizing genomic data: **rtracklayer** and **Gviz**. **rtracklayer** provides an interface to genome browsers and associated annotation tracks. **Gviz** can be used to plot coverage and annotation tracks. **TrackViewer** is a lightweight visualization tool for generating interactive figures for publication. Not only can **trackViewer** be used to visualize coverage and annotation tracks, but it can also be employed to generate lollipop/dandelion plots that depict dense methylation/mutation/variant data to facilitate an integrative analysis of these multi-omics data. It leverages **Gviz** and **rtracklayer**, is easy to use, and has a low memory and cpu consumption. In addition, we implemented a web application of **trackViewer** leveraging **Shiny** package. The web application of **trackViewer** is available at https://github.com/jianhong/trackViewer.documentation/tree/master/trackViewerShinyApp.
* [Change the styles of the track plot](changeTracksStyles.html)
* [Plot methylation/mutation/variant data by lollipop plot](lollipopPlot.html)
* [Plot high dense methylation/mutation/variant data by dandelion plot](dandelionPlot.html)
* [Plot genomic interaction data (HiC, cooler, etc) by viewTracks](plotInteractionData.html)
```{r plotComp,echo=TRUE,fig.keep='none'}
library(Gviz)
library(rtracklayer)
library(trackViewer)
extdata <- system.file("extdata", package="trackViewer",
mustWork=TRUE)
gr <- GRanges("chr11", IRanges(122929275, 122930122), strand="-")
fox2 <- importScore(file.path(extdata, "fox2.bed"), format="BED",
ranges=gr)
fox2$dat <- coverageGR(fox2$dat)
viewTracks(trackList(fox2), gr=gr, autoOptimizeStyle=TRUE, newpage=FALSE)
dt <- DataTrack(range=fox2$dat[strand(fox2$dat)=="-"] ,
genome="hg19", type="hist", name="fox2",
window=-1, chromosome="chr11",
fill.histogram="black", col.histogram="NA",
background.title="white",
col.frame="white", col.axis="black",
col="black", col.title="black")
plotTracks(dt, from=122929275, to=122930122, strand="-")
```
```{r Gviz,echo=FALSE,fig.cap='Plot data with **Gviz** and **trackViewer**. Please note that **trackViewer** can generate similar figure as **Gviz** with several lines of simple codes.',fig.width=8,fig.height=3}
viewerStyle <- trackViewerStyle()
setTrackViewerStyleParam(viewerStyle, "margin", c(.01, .13, .02, .02))
empty <- DataTrack(showAxis=FALSE, showTitle=FALSE, background.title="white")
plotTracks(list(empty, dt), from=122929275, to=122930122, strand="-")
pushViewport(viewport(0, .5, 1, .5, just=c(0, 0)))
viewTracks(trackList(fox2), viewerStyle=viewerStyle,
gr=gr, autoOptimizeStyle=TRUE, newpage=FALSE)
popViewport()
grid.text(label="Gviz track", x=.3, y=.4)
grid.text(label="trackViewer track", x=.3, y=.9)
```
**trackViewer** not only has the functionalities to produce the figures generated by **Gviz**, as shown in the Figure above, but also provides additional plotting styles as shown in the Figure below. The mimimalist design requires minimum input from the users while retaining the flexibility to change the output style easily.
```{r lostcode,echo=TRUE,fig.keep='none'}
gr <- GRanges("chr1", IRanges(c(1, 6, 10), c(3, 6, 12)), score=c(3, 4, 1))
dt <- DataTrack(range=gr, data="score", type="hist")
plotTracks(dt, from=2, to=11)
tr <- new("track", dat=gr, type="data", format="BED")
viewTracks(trackList(tr), chromosome="chr1", start=2, end=11)
```
```{r GvizLost,echo=FALSE,fig.cap='Plot data with **Gviz** and **trackViewer**. Note that **trackViewer** is not only including more details but also showing all the data involved in the given range.',fig.width=8,fig.height=3}
plotTracks(list(empty, dt), from=2, to=11)
pushViewport(viewport(0, .5, 1, .5, just=c(0, 0)))
viewTracks(trackList(tr), viewerStyle=viewerStyle,
chromosome="chr1", start=2, end=11,
autoOptimizeStyle=TRUE, newpage=FALSE)
popViewport()
grid.text(label="Gviz track", x=.3, y=.4)
grid.text(label="trackViewer track", x=.3, y=.9)
```
**Gviz** requires huge memory space to handle big wig files. To solve this problem, we rewrote the import function in **trackViewer** by importing the entire file first and parsing it later when plot. As a result, **trackViewer** decreases the import time from 180 min to 21 min and the memory cost from 10G to 5.32G for a half giga wig file (GSM917672).
# Browse
## Steps of using trackViewer \Biocpkg{trackViewer}
### Step 1. Import data
The function **importScore** is used to import BED, WIG, bedGraph or BigWig files.
The function **importBam** is employed to import the bam files. Here is an example.
```{r importData}
library(trackViewer)
extdata <- system.file("extdata", package="trackViewer",
mustWork=TRUE)
repA <- importScore(file.path(extdata, "cpsf160.repA_-.wig"),
file.path(extdata, "cpsf160.repA_+.wig"),
format="WIG")
## Because the wig file does not contain any strand info,
## we need to set it manually.
strand(repA$dat) <- "-"
strand(repA$dat2) <- "+"
```
The function **coverageGR** could be used to calculate the coverage after the data is imported.
```{r coverage}
fox2 <- importScore(file.path(extdata, "fox2.bed"), format="BED",
ranges=GRanges("chr11", IRanges(122830799, 123116707)))
dat <- coverageGR(fox2$dat)
## We can split the data by strand into two different track channels
## Here, we set the dat2 slot to save the negative strand info.
fox2$dat <- dat[strand(dat)=="+"]
fox2$dat2 <- dat[strand(dat)=="-"]
```
### Step 2. Build the gene model
The gene model can be built for a given genomic range using **geneModelFromTxdb** function which uses the **TranscriptDb** object as the input.
```{r geneModel}
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
gr <- GRanges("chr11", IRanges(122929275, 122930122), strand="-")
trs <- geneModelFromTxdb(TxDb.Hsapiens.UCSC.hg19.knownGene,
org.Hs.eg.db,
gr=gr)
```
Users can generate a track object with the geneTrack function by inputting a TxDb and a list of gene Entrez IDs. Entrez IDs can be obtained from other types of gene IDs such as gene symbol by using the ID mapping function. For example, to generate a track object given gene FMR1 and human TxDb, refer to the code below.
```{r geneTrack}
entrezIDforFMR1 <- get("FMR1", org.Hs.egSYMBOL2EG)
theTrack <- geneTrack(entrezIDforFMR1,TxDb.Hsapiens.UCSC.hg19.knownGene)[[1]]
```
### Step 3. View the tracks
Use **viewTracks** function to plot data and annotation information along genomic coordinates.
**addGuideLine** or **addArrowMark** can be used to highlight a specific region.
```{r viewTracks,fig.cap='plot data and annotation information along genomic coordinates',fig.width=8,fig.height=3}
viewerStyle <- trackViewerStyle()
setTrackViewerStyleParam(viewerStyle, "margin", c(.1, .05, .02, .02))
trackList <- trackList(repA, fox2, trs)
vp <- viewTracks(trackList,
gr=gr, viewerStyle=viewerStyle,
autoOptimizeStyle=TRUE)
addGuideLine(c(122929767, 122929969), vp=vp)
addArrowMark(list(x=122929650,
y=2), # 2 means track 2 from the bottom.
label="label",
col="blue",
vp=vp)
```
For more details, please refer [vignette changeTracksStyles](changeTracksStyles.html).
## Generate and edit an interactive plot using the browseTracks function
As shown above, figures produced by trackViewer are highly customizable, allowing users to alter the label, symbol, color, and size with various functions.
For users who prefer to modify the look and feel of a figure interactively, they can use the function `browseTracks` to draw interactive tracks, leveraging the htmlwidgets package.
```{r browseTrack,fig.cap='interactive tracks',fig.width=6,fig.height=4}
browseTracks(trackList, gr=gr)
```
The videos at https://youtu.be/lSmeTu4WMlc and https://youtu.be/lvF0tnJiHQI illustrate how to generate and modify an interactive plot. Please note that the interactive feature is only fully implemented in version 1.19.14 or later.
## Plot multiple genes in one track
```{r plotgeneTrack,fig.cap='Plot multiple genes in one track',fig.width=6,fig.height=4}
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
grW <- parse2GRanges("chr11:122,830,799-123,116,707")
ids <- getGeneIDsFromTxDb(grW, TxDb.Hsapiens.UCSC.hg19.knownGene)
symbols <- mget(ids, org.Hs.egSYMBOL)
genes <- geneTrack(ids, TxDb.Hsapiens.UCSC.hg19.knownGene,
symbols, asList=FALSE)
optSty <- optimizeStyle(trackList(repA, fox2, genes), theme="safe")
trackListW <- optSty$tracks
viewerStyleW <- optSty$style
viewTracks(trackListW, gr=grW, viewerStyle=viewerStyleW)
```
# Operators
If you are interested in drawing a combined track from two input tracks, e.g, adding or substractiong one from the other, then you can try one of the operators such as + and - as showing below.
```{r viewTracksOperator1,fig.cap='show data with operator "+"',fig.width=8,fig.height=4}
newtrack <- repA
## Must keep the same format for dat and dat2
newtrack <- parseWIG(newtrack, "chr11", 122929275, 122930122)
newtrack$dat2 <- newtrack$dat
newtrack$dat <- fox2$dat2
setTrackStyleParam(newtrack, "color", c("blue", "red"))
viewTracks(trackList(newtrack, trs),
gr=gr, viewerStyle=viewerStyle, operator="+")
```
```{r viewTracksOperator2,fig.cap='show data with operator "-"',fig.width=8,fig.height=4}
viewTracks(trackList(newtrack, trs), gr=gr, viewerStyle=viewerStyle, operator="-")
```
Alternatively, you can try **GRoperator** before viewing tracks.
```{r viewTracksOperator3,fig.cap='show data with operator "-"',fig.width=8,fig.height=4}
newtrack$dat <- GRoperator(newtrack$dat, newtrack$dat2, col="score", operator="-")
newtrack$dat2 <- GRanges()
viewTracks(trackList(newtrack, trs), gr=gr, viewerStyle=viewerStyle)
```
# Lolliplot
The `lolliplot` function is for the visualization of the methylation/variant/mutation data.
For more details, please refer [vignette lollipopPlot](lollipopPlot.html).
```{r}
library(trackViewer)
features <- GRanges("chr1", IRanges(c(1, 501, 1001),
width=c(120, 400, 405),
names=paste0("block", 1:3)),
fill = c("#FF8833", "#51C6E6", "#DFA32D"),
height = c(0.02, 0.05, 0.08))
SNP <- c(10, 100, 105, 108, 400, 410, 420, 600, 700, 805, 840, 1400, 1402)
sample.gr <- GRanges("chr1", IRanges(SNP, width=1, names=paste0("snp", SNP)),
color = sample.int(6, length(SNP), replace=TRUE),
score = sample.int(5, length(SNP), replace = TRUE))
lolliplot(sample.gr, features)
```
# Dandelion Plot
Dandelion Plot is used to plot high dense of mutation/methylation data.
For more details, please refer [vignette dandelionPlot](dandelionPlot.html).
```{r fig.width=4.5,fig.height=3}
library(trackViewer)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
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")
```
# Plot chromatin interactions data
Plot chromatin interactions heatmap as tracks.
For more details, please refer [vignette plotInteractionData](plotInteractionData.html).
```{r}
library(InteractionSet)
gi <- readRDS(system.file("extdata", "nij.chr6.51120000.53200000.gi.rds", package="trackViewer"))
head(gi)
range <- GRanges("chr6", IRanges(51120000, 53200000))
tr <- gi2track(gi)
ctcf <- readRDS(system.file("extdata", "ctcf.sample.rds",
package="trackViewer"))
## change the color
setTrackStyleParam(tr, "breaks",
c(seq(from=0, to=50, by=10), 200))
setTrackStyleParam(tr, "color",
c("lightblue", "yellow", "red"))
viewTracks(trackList(ctcf, tr, heightDist=c(1, 3)), gr=range,
autoOptimizeStyle = TRUE)
```
# Ideogram Plot
Plot ideograms with a list of chromosomes and a genome.
```{r eval=FALSE}
ideo <- loadIdeogram("hg38", chrom=c("chr1", "chr3", "chr4", 'chr21', "chr22"))
```
```{r echo=FALSE, results="hide"}
path <- system.file("extdata", "ideo.hg38.rds", package = "trackViewer")
ideo <- readRDS(path)
```
```{r, eval=FALSE}
dataList <- trim(ideo)
dataList$score <- as.numeric(as.factor(dataList$gieStain))
dataList <- dataList[dataList$gieStain!="gneg"]
dataList <- GRangesList(dataList)
ideogramPlot(ideo, dataList,
layout=list("chr1", c("chr3", "chr22"),
c("chr4", "chr21")))
```
# Web application of **trackViewer**
We created a web application of **trackViewer** (available in 1.19.14 or later) by leveraging the R package **Shiny**. The web application of trackViewer and sample data are available at https://github.com/jianhong/trackViewer.documentation/tree/master/trackViewerShinyApp. Here is a demo on how to to use the web application at https://www.nature.com/articles/s41592-019-0430-y#Sec2 Supplementary Video 5.
# Session Info
```{r sessionInfo, results='asis'}
sessionInfo()
```