Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets

April 10, 2021 · View on GitHub

Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets

HiCcompareWorkshop introduces methods for the comparative (aka differential) analysis of the three-dimensional (3D) structure of the genome using data generated by high-throughput chromatin conformation capture (Hi-C) technologies. Hi-C data allows for insights into the genome-wide 3D genomic interactions which play an important role in regulating gene expression and other genomic processes. Just as differential expression analyses using RNA-seq data have become a routine part of genomic experiments, we expect the differential analysis of genomic interactions to become a common task. This workflow will help a novice learn to perform differential analysis of two or more Hi-C datasets and interpret the differential genomic interactions' results.

This workshop is based on Stansfield et al., “R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets.”.

This workshop was presented at the Bioconductor Virtual Conference 2020, July 30, 2020, 10:00-10:55am

Using Docker

The easiest way to get started with the workshop is to run it from a Docker container.

Pull the latest version of the HiCcompareWorkshop Docker image, docker pull mdozmorov/hiccompareworkshop:latest
Run docker run -e PASSWORD=yourpassword -p 8787:8787 -d --rm mdozmorov/hiccompareworkshop. Use -v $(pwd):/home/rstudio argument to map your local directory to the container.
Log in to RStudio at http://localhost:8787 using username rstudio and password yourpassword. Note that on Windows you need to provide your localhost IP address like http://191.163.92.108:8787/ - find it using docker-machine ip default in Docker's terminal.
Run browseVignettes(package = "HiCcompareWorkshop"). Click on one of the links, "HTML", "source", "R code".
- In case of The requested page was not found error, add help/ to the URL right after the hostname, e.g., http://localhost:8787/help/library/HiCcompareWorkshop/doc/hic_tutorial.html. This is a known bug.

Local Installation

if(!require(devtools)) install.packages("devtools")
devtools::install_github(repo = "mdozmorov/HiCcompareWorkshop", build_vignettes = TRUE)

If installation fails due to missing packages, install them as follows:

if(!require(BiocManager)) install.packages("BiocManager")
BiocManager::install(c('edgeR', 'HiCcompare', 'multiHiCcompare', 'clusterProfiler', 'ROntoTools'))

Workshop Description

Instructor name and contact information

Mikhail Dozmorov (mikhail.dozmorov@vcuhealth.org)

Pre-requisites

Basic knowledge of R syntax and command-line tools
Familiarity with Hi-C chromatin conformation capture technology
Understanding of Hi-C data properties (distance-dependent decay of interaction frequencies, biases)
Familiarity with specialized (.hic http://aidenlab.org/data.html, .cool ftp://cooler.csail.mit.edu/coolers) and text-based (sparse upper-triangular, full square matrix) Hi-C data formats is desirable

R / Bioconductor packages used

Time outline

The workshop duration is 55 min. Approximate timing of activities:

Activity	Time
Overview	5m
Data representation and manipulation	10m
Differential analysis of Hi-C data	10m
Interpretation of Hi-C differences	15m
Questions and answers session	15m

Learning Goals

Get familiar with Hi-C data import into R
Understand the visualization of biases between pairs of Hi-C datasets using Mean-Distance (MD) plot
Learn the Loess-based normalization strategy that minimizes between-dataset differences
Perform differential analysis of $2 \times 2$ groups of Hi-C data, accounting for covariates, if needed
Visualize and understand interaction frequency differences
Get familiar with approaches for interpretation of chromatin interaction differences in the context of genes and gene expression differences