---
title: "`EpiCompare`: Getting started"
author: "<h5><i>Authors</i>: Sera Choi</h5>"
date: "<h5><i>Updated</i>: `r format(Sys.Date(), '%b-%d-%Y')`</h5>"
csl: nature.csl
output:
  BiocStyle::html_document:
vignette: >
    %\VignetteIndexEntry{EpiCompare} 
    %\usepackage[utf8]{inputenc}
    %\VignetteEngine{knitr::rmarkdown} 
---

# Overview

The *EpiCompare* package is designed to facilitate the comparison of epigenomic 
datasets for quality control and benchmarking purposes. The package combines 
several downstream analysis tools for epigenomic data and generates a single 
report that collates all results of the analysis. This allows users to conduct 
downstream analysis of multiple epigenomic datasets simultaneously and compare
the results in a simple and efficient way. 

## Introduction

For many years, ChIP-seq has been the standard method for epigenomic profiling,
but it suffers from a host of limitations. Recently, many other epigenomic 
technologies (e.g. CUT&Run, CUT&Tag and TIP-seq etc.), designed to overcome 
these constraints, have been developed. To better understand the performance of 
these novel approaches, it is important that we systematically compare these 
technologies and benchmark against a “gold-standard”.

There are many tools in R (e.g. *ChIPseeker*) that can be used to conduct 
downstream analysis and comparison of epigenomic datasets. However, these are 
often scattered across different packages and difficult to use for researchers
with none or little computational experience. 

*EpiCompare* is designed to provide a simple and comprehensive way of analysing
and comparing epigenomic datasets. It combines many useful downstream analysis
tools, which can easily be controlled by users and it collates the results in a
single report. This allows researchers to systematically compare different
epigenomic technologies.

While the main functionality of *EpiCompare* is to contrast epigenomic
technologies, it can also be used to compare datasets generated using different
experimental conditions and data analysis workflows of one technology. This
can help researchers to establish a consensus regarding the optimal use of the 
method. 

Currently, *EpiCompare* only works for human genome as it uses human-based 
hg19 and/or hg38 genome references. 

# Data 

The *EpiCompare* package contains a small subset of histone mark H3K27ac profile
data obtained/generated from: 

* ENCODE (data accession: ENCFF044JNJ)
* CUT&Tag from Kaya-Okur et al., (2019). (PMID: 31036827)
* CUT&Run from Meers et al., (2019). (PMID: 31232687)

It also contains human genome hg19 and hg38 blacklisted regions obtained from 
ENCODE. The ENCODE blacklist includes regions of the genome that have anomalous 
and/or unstructured signals independent of the cell-line or experiment. Removal 
of ENCODE blacklist is recommended for quality measure.

These dataset will be used to showcase *EpiCompare* functionality

# Installation

To install the package, run the following: 

```R
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("EpiCompare") 
```

# Running *EpiCompare*

In this example analysis, we will compare CUT&Run and CUT&Tag of histone mark
H3K27ac against ENCODE ChIP-seq. 

## Load package and example datasets 

Once installed, load the package: 

```{r setup_vignette}
library(EpiCompare)
```

Load example datasets used in this analysis:

```{r}
data("encode_H3K27ac") # ENCODE ChIP-seq
data("CnT_H3K27ac") # CUT&Tag
data("CnR_H3K27ac") # CUT&Run
data("hg19_blacklist") # hg19 genome blacklist 
data("CnT_H3K27ac_picard") # CUT&Tag Picard summary output
data("CnR_H3K27ac_picard") # CUT&Run Picard summary output
```

## Prepare input data

### Peaklist

*EpiCompare* accepts datasets both as `GRanges` object and as paths to BED 
files. Peakfiles (`GRanges` or paths) that you would like to analyse must be
listed and named (see below). 

```R
# To import BED files as GRanges object
peak_GRanges <-ChIPseeker::readPeakFile("/path/to/peak/file.bed",as = "GRanges")
# EpiCompare also accepts paths (to BED files) as input 
peak_path <- "/path/to/BED/file1.bed"
# Create named peak list
peaklist <- list(peak_GRanges, peak_path)
names(peaklist) <- c("sample1", "sample2")
```

In this example, we will use built-in data, which have been converted into 
`GRanges` object previously (`CnT_H3K27ac` and `CnR_H3K27ac`). 

```{r}
peaklist <- list(CnT_H3K27ac, CnR_H3K27ac) # create list of peakfiles 
names(peaklist) <- c("CnT", "CnR") # set names 
```

### Blacklist 

ENCODE blacklist contains regions of the genome that have anomalous 
and/or unstructured signals independent of the cell-line or experiment. Removal 
of these regions from peakfiles is recommended for quality measure.

*EpiCompare* contains two blacklist files, hg19 and hg38, downloaded from
[here](https://github.com/Boyle-Lab/Blacklist).

In this example analysis, since all peakfiles (`encode_H3K27ac`, `CnT_H3K27ac`,
`CnR_H3K27ac`)  were generated using human genome reference build hg19, 
`hg19_blacklist` will be used. For hg38, use `data(hg38_blacklist)`.

Please ensure that you specify the correct blacklist. 

### Picard summary files

Note that this is *OPTIONAL*. If you want the report to include metrics on
DNA fragments (e.g. mapped fragments and duplication rate), please input summary
files from Picard. 

[Picard *MarkDuplicates*](https://broadinstitute.github.io/picard/command-line-overview.html#MarkDuplicates) can be used to mark duplicate reads that are found within the alignment. This
tool outputs a metrics file with the ending `.MarkDuplicates.metrics.txt`. To
import this text file into R as data frame, use:

```R
picard <- read.table("/path/to/Picard/.MarkDuplicates.metrics.txt", header = TRUE, fill = TRUE)
```

In this example. we will use built-in data, which have been converted into data
frame previously (`CnT_H3K27ac_picard` and `CnR_H3K27ac_picard`). The files
must be listed and named: 

```{r}
# create list of Picard summary
picard <- list(CnT_H3K27ac_picard, CnR_H3K27ac_picard) 
names(picard) <- c("CnT", "CnR") # set names 
```

### Reference file

This is OPTIONAL. If reference peak file is provided, `stat_plot` and 
`chromHMM_plot` of overlapping peaks are included in the report (see 
*Optional plots* section below).

Reference file must be listed and named. In this example, we will use built-in 
data (`encode_H3K27ac`), which has been converted to `GRanges` previously:

```{r}
reference_peak <- list("ENCODE_H3K27ac" = encode_H3K27ac)
```

### Output Directory 

When running `EpiCompare()`, please ensure that you specify `output_dir`. All
outputs (figures and HTML report) will be saved in the specified `output_dir`.

## Run EpiCompare

Running EpiCompare is done using the function, `EpiCompare()` . Users can choose
which analyses to run and include in the report by setting parameters to `TRUE` 
or `FALSE`.

```{r}
EpiCompare(peakfiles = peaklist,
           genome_build = "hg19",
           blacklist = hg19_blacklist,
           picard_files = picard,
           reference = reference_peak,
           upset_plot = FALSE,
           stat_plot = FALSE,
           chromHMM_plot = FALSE,
           chromHMM_annotation = "K562",
           chipseeker_plot = FALSE,
           enrichment_plot = FALSE,
           tss_plot = FALSE,
           interact = FALSE,
           save_output = FALSE,
           output_filename = "EpiCompare_test",
           output_timestamp = FALSE,
           output_dir = tempdir())
```

### Optional plots

By default, these plots will not be included in the report unless set `TRUE`.

* `upset_plot` : Upset plot showing the number of overlapping peaks between
samples. *EpiCompare* uses `UpSetR` package.  
* `stat_plot` : A `reference` peakfile must be included for this plot. The plot
displays distribution of statistical significance (q-values) of sample peaks
that are overlapping/non-overlapping with the `reference` dataset. 
* `chromHMM_plot` : [ChromHMM](http://compbio.mit.edu/ChromHMM/#:~:text=ChromHMM%20is%20software%20for%20learning,and%20spatial%20patterns%20of%20marks.)
annotation of peaks. If `reference` is provided, ChromHMM annotation of 
overlapping and non-overlapping peaks with the `reference` is also included in 
the report. 
* `chipseeker_plot` : `ChIPseeker` functional annotation of peaks.
* `enrichment_plot` : KEGG pathway and GO enrichment analysis of peaks.
* `tss_plot` : Peak frequency around (+/- 3000bp) transcriptional start site.
Note that it may take awhile to generate this plot for large sample sizes.

### Other options 

* `chromHMM_annotation` : Cell-line annotation for ChromHMM. Default is K562. 
Options are: 
  + "K562" = K-562 cells
  + "Gm12878" = Cellosaurus cell-line GM12878
  + "H1hesc" = H1 Human Embryonic Stem Cell
  + "Hepg2" = Hep G2 cell
  + "Hmec" = Human Mammary Epithelial Cell
  + "Hsmm" = Human Skeletal Muscle Myoblasts
  + "Huvec" = Human Umbilical Vein Endothelial Cells
  + "Nhek" = Normal Human Epidermal Keratinocytes
  + "Nhlf" = Normal Human Lung Fibroblasts
* `interact` : By default, all heatmaps (percentage overlap and 
ChromHMM heatmaps) in the report will be interactive. If set FALSE, all heatmaps
will be static. N.B. If `interact=TRUE`, interactive heatmaps will be saved as 
html files, which may take time for larger sample sizes.
* `output_filename` : By default, the report is named EpiCompare.html. You can 
specify the filename of the report here. 
* `output_timestamp` : By default FALSE. If TRUE, the filename of the report 
includes the date. 

## Output

*EpiCompare* outputs

* An HTML report consisting of three sections:
  + 1. **General Metrics**: Metrics on peaks (percentage of blacklisted and 
  non-standard peaks, and peak widths) and fragments (duplication rate) of 
  samples.
  + 2. **Peak Overlap**: Percentage and statistical significance of overlapping
  and non-overlapping peaks. Also includes upset plot.
  + 3. **Functional Annotation**: Functional annotation (ChromHMM, ChIPseeker 
  and enrichment analysis) of peaks. Also includes peak enrichment around TSS.
* `EpiCompare_file` containing all plots generated by EpiCompare if 
`save_output = TRUE`. 

Both outputs are saved in the specified `output_dir`. 

# Future Enhancements 

In the current version, *EpiCompare* only recognizes certain BED formats. We hope
to improve this. Moreover, if there are other downstream analysis tools that may
be suitable in *EpiCompare*, feel free to report this through 
[Github](https://github.com/neurogenomics/EpiCompare).  

# Session Information

<details>

```{r Session_Info_vignette}
utils::sessionInfo()
```

</details>

# Contact
 
### [Neurogenomics Lab](https://www.neurogenomics.co.uk/inst/report/EpiCompare.html)

UK Dementia Research Institute  
Department of Brain Sciences  
Faculty of Medicine  
Imperial College London   
[GitHub](https://github.com/neurogenomics)  
[DockerHub](https://hub.docker.com/orgs/neurogenomicslab)  

<br>




