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https://github.com/poisonalien/trackplot
Generate IGV style locus tracks from bigWig files in R
https://github.com/poisonalien/trackplot
bigwigs igv-like locus ucsc-browser
Last synced: about 8 hours ago
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Generate IGV style locus tracks from bigWig files in R
- Host: GitHub
- URL: https://github.com/poisonalien/trackplot
- Owner: PoisonAlien
- Created: 2020-11-24T16:21:42.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-24T14:26:21.000Z (2 months ago)
- Last Synced: 2024-10-24T16:14:56.125Z (2 months ago)
- Topics: bigwigs, igv-like, locus, ucsc-browser
- Language: R
- Homepage:
- Size: 734 KB
- Stars: 136
- Watchers: 3
- Forks: 16
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
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README
## trackplot - Fast and easy visualisation of bigWig files in R
[](https://github.com/poisonalien/trackplot/issues)
## Introduction
`trackplot.R` is an ultra-fast, simple, and minimal dependency R script to generate IGV style track plots (aka locus plots), profile plots and heatmaps from bigWig files.
## Installation
`trackplot.R` is a standalone R script and requires no installation. Just source it and you're good to go!
```r
source("https://github.com/PoisonAlien/trackplot/blob/master/R/trackplot.R?raw=true")
# OR
download.file(url = "https://raw.githubusercontent.com/PoisonAlien/trackplot/master/R/trackplot.R", destfile = "trackplot.R")
source('trackplot.R')
# OR If you prefer to have it as package
remotes::install_github(repo = "poisonalien/trackplot")
```
## Features
Why `trackplot`?
* It's extremely fast since most of the heavy lifting is done by [bwtool](https://github.com/CRG-Barcelona/bwtool). >15X faster than [deeptools](https://deeptools.readthedocs.io/en/develop/) for equivalent `profileplots` and `heatmaps`
* Lightweight and minimal dependency
- [data.table](https://cran.r-project.org/web/packages/data.table/index.html) and [bwtool](https://github.com/CRG-Barcelona/bwtool) are the only requirements. Similar R packages [GViz](https://bioconductor.org/packages/release/bioc/html/Gviz.html) and [karyoploteR](http://bioconductor.org/packages/release/bioc/html/karyoploteR.html) has over 150 dependencies.
- Plots are generated in pure base R graphics (no ggplot2 or tidyverse packages)
* Automatically queries UCSC genome browser for gene models, cytobands, and chromHMM tracks - making analysis reproducible.
* Supports GTF and standard UCSC gene formats as well.
* Customization: Each plot can be customized for color, scale, height, width, etc.
* Tracks can be summarized per condition (by mean, median, max, min)
* PCA and, optional differential peak analysis with `limma` when using uniformly processed, normalized bigWig files.
## Dependencies
1. [data.table](https://cran.r-project.org/web/packages/data.table/index.html) R package - which itself has no dependency.
2. [bwtool](https://github.com/CRG-Barcelona/bwtool) - a command line tool for processing bigWig files. Install and move the binary to a PATH (e.g; `/usr/local/bin`) or a directory under the PATH.
* For macOS: Please download the pre-build binary from [here](https://www.dropbox.com/s/kajx9ya6erzyrim/bwtool_macOS.tar.gz?dl=1). Make it executable with `chmod +x bwtool`. macOS gatekeeper might complain that it can't run the binary downloaded from the internet. If so, [allow](https://support.apple.com/en-us/HT202491) it in the security settings.
* For centOS or debian: Follow these [compilation instructions](https://gist.github.com/PoisonAlien/e19b482ac6146bfb03142a0de1c4fbc8).
### Citation
If you find the script useful consider citing [trackplot](https://academic.oup.com/bioinformaticsadvances/article/4/1/vbae031/7616126) and [bwtool](https://academic.oup.com/bioinformatics/article/30/11/1618/282756)
**_Mayakonda A, and Frank Westermann. Trackplot: a fast and lightweight R script for epigenomic enrichment plots. Bioinformatics advances vol. 4,1 vbae031. 28 Feb. 2024. PMID: [38476298](https://pubmed.ncbi.nlm.nih.gov/38476298/)_**
**_Pohl A, Beato M. bwtool: a tool for bigWig files. Bioinformatics. 2014 Jun 1;30(11):1618-9. doi: 10.1093/bioinformatics/btu056. Epub 2014 Jan 30. PMID: [24489365](https://pubmed.ncbi.nlm.nih.gov/24489365/)_**
## Usage
Simple usage - Make a table of all the bigWig files to be analysed with `read_coldata()` and pass it to the downstream functions.
```mermaid
flowchart TD
a[bigWig file list] -->A{read_coldata}
A --> B{track_extract}
B --> B1[track_plot]
A --> C{profile_extract}
C --> C1[profile_summarize]
C --> C3[profile_heatmap]
C1 --> C2[profile_plot]
A --> D{extract_summary}
D --> D1[pca_plot]
D --> D2[diffpeak]
D2 --> D3[volcano_plot]
```
```r
#Path to bigWig files
bigWigs = c("H1_Oct4.bw", "H1_Nanog.bw", "H1_k4me3.bw",
"H1_k4me1.bw", "H1_k27ac.bw", "H1_H2az.bw", "H1_Ctcf.bw")
#Make a table of bigWigs along with ref genome build
bigWigs = read_coldata(bws = bigWigs, build = "hg19")
```
## trackplots
`track_extract()` and `track_plot()` are two functions to generate IGV style track plots (aka locus plots) from bigWig files. Additionally, `track_summarize` can summarize tracks by condition.
### Step-1: Extract signal from bigWig files
```r
#Region to plot
oct4_loci = "chr6:31125776-31144789"
#Extract bigWig signal for a loci of interest
t = track_extract(colData = bigWigs, loci = oct4_loci)
#Or you can also specifiy a gene name instead of a loci
# - loci and gene models will be automatically extracted from UCSC genome browser
t = track_extract(colData = bigWigs, gene = "POU5F1")
```
### Step-2: Plot
#### Basic plot
```r
track_plot(summary_list = t)
```
#### Add cytoband and change colors for each track
```r
track_cols = c("#d35400","#d35400","#2980b9","#2980b9","#2980b9", "#27ae60","#27ae60")
track_plot(summary_list = t,
col = track_cols,
show_ideogram = TRUE)
```
#### Collapse all tracks into a single track
Use `track_overlay = TRUE` to overlay all tracks into a single line track
```r
track_plot(summary_list = t, col = track_cols, show_ideogram = FALSE, track_overlay = TRUE)
```
#### Heighilight regions of interest (any bed files would do)
```r
oct4_nanog_peaks = c("H1_Nanog.bed","H1_Oct4.bed") #Peak files
track_plot(summary_list = t,
col = track_cols,
show_ideogram = TRUE,
peaks = oct4_nanog_peaks)
```
#### Add some chromHMM tracks to the bottom
chromHMM data should be a bed file with the 4th column containing chromatin state. See here for an [example](https://github.com/PoisonAlien/trackplot/blob/master/inst/extdata/narrowpeak/H1_chromHMM.bed) file.
Note that the color code for each of the 15 states are as described [here](https://genome.ucsc.edu/cgi-bin/hgTrackUi?g=wgEncodeBroadHmm&db=hg19).
In case if it is different for your data, you will have to define your own color codes for each state and pass it to the argument `chromHMM_cols`
```r
chromHMM_peaks = "H1_chromHMM.bed"
track_plot(summary_list = t,
col = track_cols,
show_ideogram = TRUE,
peaks = oct4_nanog_peaks, chromHMM = chromHMM_peaks)
```
#### Add some chromHMM tracks from UCSC
UCSC has 9 cell lines for which chromHMM data is available. These can be added automatically in case if you dont have your own data.
In this case, use the argument `ucscChromHMM` with any values from TableName column of the below table.
```r
TableName cell Description Tissue Karyotype
1: wgEncodeBroadHmmGm12878HMM GM12878 B-lymphocyte, lymphoblastoid blood normal
2: wgEncodeBroadHmmH1hescHMM H1-hESC embryonic stem cells embryonic stem cell normal
3: wgEncodeBroadHmmHepg2HMM HepG2 hepatocellular carcinoma liver cancer
4: wgEncodeBroadHmmHepg2HMM HMEC mammary epithelial cells breast normal
5: wgEncodeBroadHmmHsmmHMM HSMM skeletal muscle myoblasts muscle normal
6: wgEncodeBroadHmmHuvecHMM HUVEC umbilical vein endothelial cells blood vessel normal
```
```r
track_plot(summary_list = t,
col = track_cols,
show_ideogram = TRUE,
peaks = oct4_nanog_peaks,
ucscChromHMM = c("wgEncodeBroadHmmH1hescHMM", "wgEncodeBroadHmmNhlfHMM"))
```
### Re-organize tracks
By default tracks are organized from top to bottom as `c("p", "b", "h", "g", "c")` corresponding to peaks track, bigWig track, chromHmm track, gene track, and cytoband track. This can be changes with the argument `layout_ord`. Furthermore, bigWig tracks themselves can be ordered with the argument `bw_ord` which accepts the names of the bigWig tracks as input and plots them in the given order.
```r
#Draw only NANOG, OCT4 bigWigs in that order. Re-organize the layout in the order chromHMM track, gene track, cytoband track. Rest go to the end.
track_plot(
summary_list = t,
col = track_cols,
show_ideogram = TRUE,
genename = c("POU5F1", "TCF19"),
peaks = oct4_nanog_peaks,
peaks_track_names = c("NANOG", "OCT4"),
groupAutoScale = FALSE, ucscChromHMM = "wgEncodeBroadHmmH1hescHMM", y_min = 0,
bw_ord = c("NANOG", "OCT4"),
layout_ord = c("h", "g", "c")
)
```
## narrowPeaks and broadPeaks
All of the above plots can also be generated with [narrowPeak](https://genome.ucsc.edu/FAQ/FAQformat.html#format12) or [broadPeak](https://genome.ucsc.edu/FAQ/FAQformat.html#format13) files as input. Here, 5th column containing scores are plotted as intensity. Color coding and binning of scores are as per [UCSC convention](https://genome.ucsc.edu/FAQ/FAQformat.html#format1)
`narrowPeak` is one of the output from macs2 peak caller and are easier to visualize in the absence of bigWig files.
```r
narrowPeaks = c("H1_Ctcf.bed", "H1_H2az.bed", "H1_k27ac.bed",
"H1_k4me1.bed", "H1_k4me3.bed", "H1_Nanog.bed",
"H1_Oct4.bed", "H1_Pol2.bed")
#Use peak as input_type
narrowPeaks = read_coldata(narrowPeaks, build = "hg19", input_type = "peak")
oct4_loci = "chr6:30,818,383-31,452,182" #633Kb region for example
narrowPeaks_track = track_extract(colData = narrowPeaks, loci = oct4_loci)
#Rest plotting is same
track_plot(summary_list = narrowPeaks_track,
show_ideogram = TRUE,
peaks = oct4_nanog_peaks,
ucscChromHMM = c("wgEncodeBroadHmmH1hescHMM", "wgEncodeBroadHmmNhlfHMM"))
```
## profileplots
`profile_extract()` -> `profile_summarize()` -> `profile_plot()` are functions to generate density based profile-plots from bigWig files.
* Below example for summarizing approx. 3,671 peaks for 3 bigWig files takes ca. 3 seconds on my 5 year old [macbook Pro](https://support.apple.com/kb/sp715?locale=en_GB). This includes generating signal matrix, summarizing, and plotting. Equivalent deeptools commands takes 20 seconds.
* Optionally, it can also query UCSC genome browser for refseq transcripts of desired assembly and summarize around TSS regions
* Replicates can be collapsed into single value per condition
Example data from [GSE99183](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE99183) where U87 glioma cell lines are treated with a DMSO and a BRD4 degradaer.
```r
bws = c("GSM2634756_U87_BRD4.bw", "GSM2634757_U87_BRD4_dBET_24h.bw", "GSM2634758_U87_BRD4_dBET_2h.bw")
bws = read_coldata(bws = bws,
sample_names = c("BRD4", "BRD4_dBET_24h", "BRD4_dBET_2h"),
build = "hg19")
```
### Refseq transcripts
```r
#Extract signals from bigWig files around refseq transcripts
pe_refseq = profile_extract(colData = bws, ucsc_assembly = TRUE,
startFrom = 'start', up = 1500, down = 1500)
#Estimate mean signal
ps_refseq = profile_summarize(sig_list = pe_refseq)
#Plot
profile_plot(ps_refseq)
```
### Custom BED regions
```r
#BRD4 binding sites
bed = "GSM2634756_U87_BRD4_peaks.narrowPeak.gz"
#Center and extend 1500 both ways from the peak center
pe_bed = profile_extract(colData = bws, bed = bed, startFrom = "center",
up = 1500, down = 1500, nthreads = 4)
#Estimate mean signal
ps_bed = profile_summarize(sig_list = pe_bed)
#Plot
profile_plot(ps_bed)
```
## heatmap
Output from `profile_extract` can be used to draw a heatmap with `profile_heatmap`
```r
profile_heatmap(mat_list = pe_bed, top_profile = TRUE, zmaxs = 0.8)
```
***PSA*** If you find the tool useful, consider starring this repository or up voting this [Biostars thread](https://www.biostars.org/p/475853/) so that more poeple can find it :)
### Caveat
* Windows OS is not supported
### Acknowledgements
[Joschka Hey](https://github.com/HeyLifeHD) for all the cool suggestions :)