HiCExperiment

👉 OHCA book 📖

Please cite:

Serizay J, Matthey-Doret C, Bignaud A, Baudry L, Koszul R (2024). “Orchestrating chromosome conformation capture analysis with Bioconductor.” Nature Communications, 15, 1-9. doi:10.1038/s41467-024-44761-x.

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The HiCExperiment package provides a unified data structure to import the three main Hi-C matrix file formats (.(m)cool, .hic and HiC-Pro matrices) in R and performs common array operations on them.

The HiCExperiment class wraps an (indexed) matrix-like object (i.e. on-disk .(m)cool, .hic or HiC-Pro matrices). For indexed matrices (i.e. .(m)cool and .hic files), HiCExperiment allows one to specfically parse subsets of the contact matrix corresponding to genomic loci of interest, without having to load the entire object in memory.

The HiCExperiment package also provides methods to import pairs files generated by pairtools/cooler workflow, by HiC-Pro pipeline, or any type of tabular pairs format (by indicating the columns containing chr1, start1, strand1, chr2, start2, strand2 information).

HiCExperiment S4 class is built on pre-existing Bioconductor classes, namely BiocFile and GInteractions (Lun, Perry & Ing-Simmons, F1000Research 2016`), and leverages them to point to on-disk Hi-C matrix files and dynamically parse them into R.

Several other packages rely on the HiCExperiment class to provide a rich ecosystem when interacting with Hi-C data.

Installation

HiCExperiment is an R/Bioconductor package. As such, it can be installed with:

BiocManager::install("HiCExperiment")

Importing a Hi-C matrix file

.(m)cool files:

cool_file <- CoolFile(HiContactsData::HiContactsData('yeast_wt', format = 'cool'))
import(cool_file, focus = "II:10000-100000")

## `HiCExperiment` object with 3,454 interactions over 90 regions
## -------
## fileName: "/home/rsg/.cache/R/ExperimentHub/36d548fb47bf_7751"
## focus: "II:10,000-100,000"
## resolutions(1): 1000
## current resolution: 1000
## interactions: 3454
## scores(2): count balanced
## topologicalFeatures: loops(0) borders(0) compartments(0) viewpoints(0)
## pairsFile: N/A
## metadata(0):

mcool_file <- CoolFile(HiContactsData::HiContactsData('yeast_wt', format = 'mcool'))
import(mcool_file, focus = "II:10000-100000", resolution = 2000)

## `HiCExperiment` object with 1,004 interactions over 45 regions
## -------
## fileName: "/home/rsg/.cache/R/ExperimentHub/36d590c5583_7752"
## focus: "II:10,000-100,000"
## resolutions(5): 1000 2000 4000 8000 16000
## current resolution: 2000
## interactions: 1004
## scores(2): count balanced
## topologicalFeatures: loops(0) borders(0) compartments(0) viewpoints(0)
## pairsFile: N/A
## metadata(0):

.hic files:

hic_file <- HicFile(HiContactsData::HiContactsData('yeast_wt', format = 'hic'))
import(hic_file, focus = "II:10000-100000", resolution = 4000)

## `HiCExperiment` object with 276 interactions over 23 regions
## -------
## fileName: "/home/rsg/.cache/R/ExperimentHub/7fa45373d163_7836"
## focus: "II:10,000-100,000"
## resolutions(5): 1000 2000 4000 8000 16000
## current resolution: 4000
## interactions: 276
## scores(2): count balanced
## topologicalFeatures: loops(0) borders(0) compartments(0) viewpoints(0)
## pairsFile: N/A
## metadata(0):

HiC-Pro files:

hicpro_file <- HicproFile(
    HiContactsData::HiContactsData('yeast_wt', format = 'hicpro_matrix'),
    bed = HiContactsData::HiContactsData('yeast_wt', format = 'hicpro_bed')
)
import(hicpro_file)

## `HiCExperiment` object with 2,686,250 interactions over 11,805 regions
## -------
## fileName: "/home/rsg/.cache/R/ExperimentHub/29210052806_7837"
## focus: "whole genome"
## resolutions(1): 1000
## current resolution: 1000
## interactions: 2686250
## scores(1): counts
## topologicalFeatures: loops(0) borders(0) compartments(0) viewpoints(0)
## pairsFile: N/A
## metadata(1): regions

Importing a pairs file

• .pairs files (e.g. from pairtools or cooler):

pairs_file <- PairsFile(HiContactsData('yeast_wt', format = 'pairs.gz'))
import(pairs_file)

## GInteractions object with 471364 interactions and 4 metadata columns:
##            seqnames1   ranges1     seqnames2   ranges2 |    counts     frag1     frag2  distance
##                <Rle> <IRanges>         <Rle> <IRanges> | <integer> <numeric> <numeric> <numeric>
##        [1]        II       105 ---        II     48548 |         1      1358      1681     48443
##        [2]        II       113 ---        II     45003 |         1      1358      1658     44890
##        [3]        II       119 ---        II    687251 |         1      1358      5550    687132
##        [4]        II       160 ---        II     26124 |         1      1358      1510     25964
##        [5]        II       169 ---        II     39052 |         1      1358      1613     38883
##        ...       ...       ... ...       ...       ... .       ...       ...       ...       ...
##   [471360]        II    808605 ---        II    809683 |         1      6316      6320      1078
##   [471361]        II    808609 ---        II    809917 |         1      6316      6324      1308
##   [471362]        II    808617 ---        II    809506 |         1      6316      6319       889
##   [471363]        II    809447 ---        II    809685 |         1      6319      6321       238
##   [471364]        II    809472 ---        II    809675 |         1      6319      6320       203
##   -------
##   regions: 549331 ranges and 0 metadata columns
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

• .validPairs files (e.g. from HiC-Pro pipeline):

hicpro_pairs_file <- PairsFile(HiContactsData('yeast_wt', format = 'hicpro_pairs'))
import(hicpro_pairs_file, nrows = 100)

## GInteractions object with 100 interactions and 4 metadata columns:
##         seqnames1   ranges1     seqnames2   ranges2 |    counts     frag1       frag2  distance
##             <Rle> <IRanges>         <Rle> <IRanges> | <integer> <numeric> <character> <numeric>
##     [1]         I        33 ---         I       620 |         1       414     HIC_I_1       587
##     [2]         I        35 ---       III    301620 |         1       336     HIC_I_1        NA
##     [3]         I        41 ---         I     68853 |         1       352     HIC_I_1     68812
##     [4]         I        49 ---         I      3233 |         1       311     HIC_I_1      3184
##     [5]         I        51 ---      VIII    197898 |         1       397     HIC_I_1        NA
##     ...       ...       ... ...       ...       ... .       ...       ...         ...       ...
##    [96]         I       138 ---      VIII    326284 |         1       251     HIC_I_1        NA
##    [97]         I       141 ---         I      2466 |         1       231     HIC_I_1      2325
##    [98]         I       142 ---         I      2219 |         1       278     HIC_I_1      2077
##    [99]         I       142 ---        XI    222517 |         1       270     HIC_I_1        NA
##   [100]         I       142 ---        XV    441757 |         1       280     HIC_I_1        NA
##   -------
##   regions: 158 ranges and 0 metadata columns
##   seqinfo: 15 sequences from an unspecified genome; no seqlengths

The HiCExperiment ecosystem

HiContacts

HiContacts package further provides analytical and visualization tools to investigate Hi-C matrices imported as HiCExperiment in R.

Among other features, it provides the end-user with generic functions to annotate topological features in a Hi-C contact map and export them, notably compartments, domains of constrained interactions (so-called TADs) and focal chromatin loops.

HiCool

HiCool package integrates an end-to-end processing workflow, to generate multi-resolution balanced contact matrices from paired-end fastq files of Hi-C experiments.

Under the hood, HiCool leverages hicstuff and cooler to process fastq files into .mcool files. hicstuff takes care of the heavy-lifting, and accurately filters non-informative read pairs out, to retain only informative contacts.

Two important features of HiCool are:

1. Its operability within the R ecosystem. It relies on basilisk to set up a conda environment with pinned versions of each software it needs to align, filter and process read pairs into contact matrices.
2. Its transparency. HiCool generates QC checks and logs, all embedded in HTML files to easily inspect the quality of each sample.

fourDNData

fourDNData (read "4DN Data") provides a gateway to the 4DN data portal.

HiContactsData

HiContactsData package provides toy datasets to illustrate how the HiCExperiment ecosystem works.

Contributing

We use devtools and testthat for the development workflow. A Makefile is provided for automation. New functions should be documented with roxygen2 comments and associated tests should be added inside tests/testthat/.

• To install the package for development, run make install.
• To run tests, run make test
• To know more, run make help

For development purposes, we provide a DockerHub-hosted docker image with HiCExperiment and related packages pre-installed and ready-to-go. A new image is automatically built on every push.

## To fetch the latest docker image from Docker Hub (for development purposes!)
docker pull js2264/hicexperiment:latest
## To start docker image
docker run -it js2264/hicexperiment:latest /usr/local/bin/R

On top of that, for each release, an extra docker image is built and uploaded to the Github Container Repository.

## To fetch release-specific docker image from Github Container Repo
docker pull ghcr.io/js2264/hicexperiment:0.99.9
## To start docker image
docker run -it ghcr.io/js2264/hicexperiment:0.99.9 /usr/local/bin/R