Contents

1 Introduction

Hi-C experimental approach allows one to query contact frequency for all possible pairs of genomic loci simultaneously, in a genome-wide manner. The output of this next-generation sequencing-supported technique is a file describing every pair (a.k.a contact, or interaction) between two genomic loci. This so-called “pairs” file can be binned and transformed into a numerical matrix. In such matrix, each cell contains the raw or normalized interaction frequency between a pair of genomic loci (which location can be retrieved using the corresponding column and row indices).

HiC-Pro, distiller and Juicer are the three main pipelines used to align, filter and process paired-end fastq reads into pairs files and contact matrices. Each pipeline defined their own file formats to store these two types of files.

Each file format can contain roughly the same information, albeit with a largely improved compression for .hic and .(m)cool files, which can also contain multi-resolution matrices compared to the HiC-Pro derived files. The 4DN consortium, deciphering the role nuclear organization plays in gene expression and cellular function, officially supports both the .hic and .(m)cool formats. Furthermore, the .(m)cool format has recently gained a lot of traction with the release of a series of python packages (cooler, cooltools, pairtools, coolpuppy) by the Open2C organization facilitating the investigation of Hi-C data stored in .(m)cool files in a python environment.

The R HiCExperiment package aims at unlocking HiC investigation within the rich, genomic-oriented Bioconductor environment. It provides a set of classes and import functions to parse HiC files (both contact matrices and pairs) in R, allowing random access and efficient genome-based subsetting of contact matrices. It leverages pre-existing base Bioconductor classes, notably GInteractions and ContactMatrix classes (Lun, Perry & Ing-Simmons, F1000 Research 2016).

2 Installation

HiCExperiment package can be installed from Bioconductor using the following command:

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

BiocManager::install("HiCExperiment")

All R dependencies will be installed automatically.

3 The HiCExperiment class

library(HiCExperiment)
showClass("HiCExperiment")
#> Class "HiCExperiment" [package "HiCExperiment"]
#> 
#> Slots:
#>                                                                   
#> Name:             fileName               focus         resolutions
#> Class:           character     characterOrNULL             numeric
#>                                                                   
#> Name:           resolution        interactions              scores
#> Class:             numeric       GInteractions          SimpleList
#>                                                                   
#> Name:  topologicalFeatures           pairsFile            metadata
#> Class:          SimpleList     characterOrNULL                list
#> 
#> Extends: "Annotated"
#> 
#> Known Subclasses: "AggrHiCExperiment"
hic <- contacts_yeast()
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
hic
#> `HiCExperiment` object with 8,757,906 contacts over 763 regions 
#> -------
#> fileName: "/home/biocbuild/.cache/R/ExperimentHub/3faa875f1a0c43_7752" 
#> focus: "whole genome" 
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 16000 
#> interactions: 267709 
#> scores(2): count balanced 
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) centromeres(16) 
#> pairsFile: N/A 
#> metadata(0):

4 Basics: importing .(m)cool, .hic or HiC-Pro-generated files as HiCExperiment objects

4.1 Import methods

The implemented import() methods allow one to import Hi-C matrix files in R as HiCExperiment objects.

hic <- import(
    "path/to/contact_matrix.cool", 
    focus = "chr:start-end", 
    resolution = ...
)

To give real-life examples, we use the HiContactsData package to get access to a range of toy datasets available from the ExperimentHub.

library(HiContactsData)
cool_file <- HiContactsData('yeast_wt', format = 'cool')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
import(cool_file, format = 'cool')
#> `HiCExperiment` object with 8,757,906 contacts over 12,079 regions 
#> -------
#> fileName: "/home/biocbuild/.cache/R/ExperimentHub/3faa596df7545f_7751" 
#> focus: "whole genome" 
#> resolutions(1): 1000
#> active resolution: 1000 
#> interactions: 2945692 
#> scores(2): count balanced 
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) 
#> pairsFile: N/A 
#> metadata(0):

4.2 Supporting file classes

There are currently three main standards to store Hi-C matrices in files:

  • .(m)cool files
  • .hic files
  • .matrix and .bed files: generated by HiC-Pro.

Three supporting classes were specifically created to ensure that each of these file structures would be properly parsed into HiCExperiment objects:

  • CoolFile
  • HicFile
  • HicproFile

For each object, an optional pairsFile can be associated and linked to the contact matrix file when imported as a HiCExperiment object.

## --- CoolFile
pairs_file <- HiContactsData('yeast_wt', format = 'pairs.gz')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
coolf <- CoolFile(cool_file, pairsFile = pairs_file)
coolf
#> CoolFile object
#> .mcool file: /home/biocbuild/.cache/R/ExperimentHub/3faa596df7545f_7751 
#> resolution: 1000 
#> pairs file: /home/biocbuild/.cache/R/ExperimentHub/3faa875c0c5742_7753 
#> metadata(0):
import(coolf)
#> `HiCExperiment` object with 8,757,906 contacts over 12,079 regions 
#> -------
#> fileName: "/home/biocbuild/.cache/R/ExperimentHub/3faa596df7545f_7751" 
#> focus: "whole genome" 
#> resolutions(1): 1000
#> active resolution: 1000 
#> interactions: 2945692 
#> scores(2): count balanced 
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) 
#> pairsFile: /home/biocbuild/.cache/R/ExperimentHub/3faa875c0c5742_7753 
#> metadata(0):
import(pairsFile(coolf), format = 'pairs')
#> GInteractions object with 471364 interactions and 3 metadata columns:
#>            seqnames1   ranges1     seqnames2   ranges2 |     frag1     frag2
#>                <Rle> <IRanges>         <Rle> <IRanges> | <numeric> <numeric>
#>        [1]        II       105 ---        II     48548 |      1358      1681
#>        [2]        II       113 ---        II     45003 |      1358      1658
#>        [3]        II       119 ---        II    687251 |      1358      5550
#>        [4]        II       160 ---        II     26124 |      1358      1510
#>        [5]        II       169 ---        II     39052 |      1358      1613
#>        ...       ...       ... ...       ...       ... .       ...       ...
#>   [471360]        II    808605 ---        II    809683 |      6316      6320
#>   [471361]        II    808609 ---        II    809917 |      6316      6324
#>   [471362]        II    808617 ---        II    809506 |      6316      6319
#>   [471363]        II    809447 ---        II    809685 |      6319      6321
#>   [471364]        II    809472 ---        II    809675 |      6319      6320
#>             distance
#>            <integer>
#>        [1]     48443
#>        [2]     44890
#>        [3]    687132
#>        [4]     25964
#>        [5]     38883
#>        ...       ...
#>   [471360]      1078
#>   [471361]      1308
#>   [471362]       889
#>   [471363]       238
#>   [471364]       203
#>   -------
#>   regions: 549331 ranges and 0 metadata columns
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

## --- HicFile
hic_file <- HiContactsData('yeast_wt', format = 'hic')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
hicf <- HicFile(hic_file, pairsFile = pairs_file)
hicf
#> HicFile object
#> .hic file: /home/biocbuild/.cache/R/ExperimentHub/3faa872477db3f_7836 
#> resolution: 1000 
#> pairs file: /home/biocbuild/.cache/R/ExperimentHub/3faa875c0c5742_7753 
#> metadata(0):
import(hicf)
#> `HiCExperiment` object with 13,681,280 contacts over 12,165 regions 
#> -------
#> fileName: "/home/biocbuild/.cache/R/ExperimentHub/3faa872477db3f_7836" 
#> focus: "whole genome" 
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 1000 
#> interactions: 2965693 
#> scores(2): count balanced 
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) 
#> pairsFile: /home/biocbuild/.cache/R/ExperimentHub/3faa875c0c5742_7753 
#> metadata(0):

## --- HicproFile
hicpro_matrix_file <- HiContactsData('yeast_wt', format = 'hicpro_matrix')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
hicpro_regions_file <- HiContactsData('yeast_wt', format = 'hicpro_bed')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
hicprof <- HicproFile(hicpro_matrix_file, bed = hicpro_regions_file)
hicprof
#> HicproFile object
#> HiC-Pro files:
#>   $ matrix:   /home/biocbuild/.cache/R/ExperimentHub/3faa8720824e40_7837 
#>   $ regions:  /home/biocbuild/.cache/R/ExperimentHub/3faa87c3c8339_7838 
#> resolution: 1000 
#> pairs file: 
#> metadata(0):
import(hicprof)
#> Registered S3 methods overwritten by 'readr':
#>   method                    from 
#>   as.data.frame.spec_tbl_df vroom
#>   as_tibble.spec_tbl_df     vroom
#>   format.col_spec           vroom
#>   print.col_spec            vroom
#>   print.collector           vroom
#>   print.date_names          vroom
#>   print.locale              vroom
#>   str.col_spec              vroom
#> `HiCExperiment` object with 9,503,604 contacts over 12,165 regions 
#> -------
#> fileName: "/home/biocbuild/.cache/R/ExperimentHub/3faa8720824e40_7837" 
#> focus: "whole genome" 
#> resolutions(1): 1000
#> active resolution: 1000 
#> interactions: 2686250 
#> scores(2): count balanced 
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) 
#> pairsFile: N/A 
#> metadata(1): regions

5 Import arguments

5.1 Querying subsets of Hi-C matrix files

The focus argument is used to specifically import contacts within a genomic locus of interest.

availableChromosomes(cool_file)
#> Seqinfo object with 16 sequences from an unspecified genome:
#>   seqnames seqlengths isCircular genome
#>   I            230218       <NA>   <NA>
#>   II           813184       <NA>   <NA>
#>   III          316620       <NA>   <NA>
#>   IV          1531933       <NA>   <NA>
#>   V            576874       <NA>   <NA>
#>   ...             ...        ...    ...
#>   XII         1078177       <NA>   <NA>
#>   XIII         924431       <NA>   <NA>
#>   XIV          784333       <NA>   <NA>
#>   XV          1091291       <NA>   <NA>
#>   XVI          948066       <NA>   <NA>
hic <- import(cool_file, format = 'cool',  focus = 'I:20001-80000')
hic
#> `HiCExperiment` object with 24,322 contacts over 60 regions 
#> -------
#> fileName: "/home/biocbuild/.cache/R/ExperimentHub/3faa596df7545f_7751" 
#> focus: "I:20,001-80,000" 
#> resolutions(1): 1000
#> active resolution: 1000 
#> interactions: 1653 
#> scores(2): count balanced 
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) 
#> pairsFile: N/A 
#> metadata(0):
focus(hic)
#> [1] "I:20001-80000"

Note:
Querying subsets of HiC-Pro formatted matrices is currently not supported. HiC-Pro formatted matrices will systematically be fully imported in memory when imported.

One can also extract a count matrix from a Hi-C matrix file that is not centered at the diagonal. To do this, specify a couple of coordinates in the focus argument using a character string formatted as "...|...":

hic <- import(cool_file, format = 'cool', focus = 'II:1-500000|II:100001-300000')
focus(hic)
#> [1] "II:1-500000|II:100001-300000"

5.2 Multi-resolution Hi-C matrix files

import() works with .mcool and multi-resolution .hic files as well: in this case, the user can specify the resolution at which count values are recovered.

mcool_file <- HiContactsData('yeast_wt', format = 'mcool')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
availableResolutions(mcool_file)
#> resolutions(5): 1000 2000 4000 8000 16000
#> 
availableChromosomes(mcool_file)
#> Seqinfo object with 16 sequences from an unspecified genome:
#>   seqnames seqlengths isCircular genome
#>   I            230218       <NA>   <NA>
#>   II           813184       <NA>   <NA>
#>   III          316620       <NA>   <NA>
#>   IV          1531933       <NA>   <NA>
#>   V            576874       <NA>   <NA>
#>   ...             ...        ...    ...
#>   XII         1078177       <NA>   <NA>
#>   XIII         924431       <NA>   <NA>
#>   XIV          784333       <NA>   <NA>
#>   XV          1091291       <NA>   <NA>
#>   XVI          948066       <NA>   <NA>
hic <- import(mcool_file, format = 'cool', focus = 'II:1-800000', resolution = 2000)
hic
#> `HiCExperiment` object with 466,123 contacts over 400 regions 
#> -------
#> fileName: "/home/biocbuild/.cache/R/ExperimentHub/3faa875f1a0c43_7752" 
#> focus: "II:1-800,000" 
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 2000 
#> interactions: 33479 
#> scores(2): count balanced 
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) 
#> pairsFile: N/A 
#> metadata(0):

6 HiCExperiment accessors

6.1 Slots

Slots for a HiCExperiment object can be accessed using the following getters:

fileName(hic)
#> [1] "/home/biocbuild/.cache/R/ExperimentHub/3faa875f1a0c43_7752"
focus(hic)
#> [1] "II:1-800000"
resolutions(hic)
#> [1]  1000  2000  4000  8000 16000
resolution(hic)
#> [1] 2000
interactions(hic)
#> GInteractions object with 33479 interactions and 4 metadata columns:
#>           seqnames1       ranges1     seqnames2       ranges2 |   bin_id1
#>               <Rle>     <IRanges>         <Rle>     <IRanges> | <numeric>
#>       [1]        II        1-2000 ---        II        1-2000 |       116
#>       [2]        II        1-2000 ---        II     4001-6000 |       116
#>       [3]        II        1-2000 ---        II     6001-8000 |       116
#>       [4]        II        1-2000 ---        II    8001-10000 |       116
#>       [5]        II        1-2000 ---        II   10001-12000 |       116
#>       ...       ...           ... ...       ...           ... .       ...
#>   [33475]        II 794001-796000 ---        II 796001-798000 |       513
#>   [33476]        II 794001-796000 ---        II 798001-800000 |       513
#>   [33477]        II 796001-798000 ---        II 796001-798000 |       514
#>   [33478]        II 796001-798000 ---        II 798001-800000 |       514
#>   [33479]        II 798001-800000 ---        II 798001-800000 |       515
#>             bin_id2     count  balanced
#>           <numeric> <numeric> <numeric>
#>       [1]       116         1       NaN
#>       [2]       118         2       NaN
#>       [3]       119         3       NaN
#>       [4]       120        15       NaN
#>       [5]       121         9       NaN
#>       ...       ...       ...       ...
#>   [33475]       514       309 0.1194189
#>   [33476]       515       227 0.0956207
#>   [33477]       514       130 0.0501703
#>   [33478]       515       297 0.1249314
#>   [33479]       515       117 0.0536429
#>   -------
#>   regions: 400 ranges and 4 metadata columns
#>   seqinfo: 16 sequences from an unspecified genome
scores(hic)
#> List of length 2
#> names(2): count balanced
tail(scores(hic, 1))
#> [1] 212 309 227 130 297 117
tail(scores(hic, 'balanced'))
#> [1] 0.08204677 0.11941893 0.09562069 0.05017035 0.12493137 0.05364290
topologicalFeatures(hic)
#> List of length 4
#> names(4): compartments borders loops viewpoints
pairsFile(hic)
#> NULL
metadata(hic)
#> list()

Several extra functions are available as well:

seqinfo(hic) ## To recover the `Seqinfo` object from the `.(m)cool` file
#> Seqinfo object with 16 sequences from an unspecified genome:
#>   seqnames seqlengths isCircular genome
#>   I            230218       <NA>   <NA>
#>   II           813184       <NA>   <NA>
#>   III          316620       <NA>   <NA>
#>   IV          1531933       <NA>   <NA>
#>   V            576874       <NA>   <NA>
#>   ...             ...        ...    ...
#>   XII         1078177       <NA>   <NA>
#>   XIII         924431       <NA>   <NA>
#>   XIV          784333       <NA>   <NA>
#>   XV          1091291       <NA>   <NA>
#>   XVI          948066       <NA>   <NA>
bins(hic) ## To bin the genome at the current resolution
#> GRanges object with 6045 ranges and 2 metadata columns:
#>                     seqnames        ranges strand |    bin_id    weight
#>                        <Rle>     <IRanges>  <Rle> | <numeric> <numeric>
#>            I_1_2000        I        1-2000      * |         0 0.0559613
#>         I_2001_4000        I     2001-4000      * |         1 0.0333136
#>         I_4001_6000        I     4001-6000      * |         2 0.0376028
#>         I_6001_8000        I     6001-8000      * |         3 0.0369553
#>        I_8001_10000        I    8001-10000      * |         4 0.0220139
#>                 ...      ...           ...    ... .       ...       ...
#>   XVI_940001_942000      XVI 940001-942000      * |      6040 0.0226033
#>   XVI_942001_944000      XVI 942001-944000      * |      6041       NaN
#>   XVI_944001_946000      XVI 944001-946000      * |      6042       NaN
#>   XVI_946001_948000      XVI 946001-948000      * |      6043       NaN
#>   XVI_948001_948066      XVI 948001-948066      * |      6044       NaN
#>   -------
#>   seqinfo: 16 sequences from an unspecified genome
regions(hic) ## To extract unique regions of the contact matrix
#> GRanges object with 400 ranges and 4 metadata columns:
#>                    seqnames        ranges strand |    bin_id    weight   chr
#>                       <Rle>     <IRanges>  <Rle> | <numeric> <numeric> <Rle>
#>          II_1_2000       II        1-2000      * |       116       NaN    II
#>       II_2001_4000       II     2001-4000      * |       117       NaN    II
#>       II_4001_6000       II     4001-6000      * |       118       NaN    II
#>       II_6001_8000       II     6001-8000      * |       119       NaN    II
#>      II_8001_10000       II    8001-10000      * |       120 0.0461112    II
#>                ...      ...           ...    ... .       ...       ...   ...
#>   II_790001_792000       II 790001-792000      * |       511 0.0236816    II
#>   II_792001_794000       II 792001-794000      * |       512 0.0272236    II
#>   II_794001_796000       II 794001-796000      * |       513 0.0196726    II
#>   II_796001_798000       II 796001-798000      * |       514 0.0196450    II
#>   II_798001_800000       II 798001-800000      * |       515 0.0214123    II
#>                       center
#>                    <integer>
#>          II_1_2000      1000
#>       II_2001_4000      3000
#>       II_4001_6000      5000
#>       II_6001_8000      7000
#>      II_8001_10000      9000
#>                ...       ...
#>   II_790001_792000    791000
#>   II_792001_794000    793000
#>   II_794001_796000    795000
#>   II_796001_798000    797000
#>   II_798001_800000    799000
#>   -------
#>   seqinfo: 16 sequences from an unspecified genome
anchors(hic) ## To extract "first" and "second" anchors for each interaction
#> $first
#> GRanges object with 33479 ranges and 4 metadata columns:
#>           seqnames        ranges strand |    bin_id    weight   chr    center
#>              <Rle>     <IRanges>  <Rle> | <numeric> <numeric> <Rle> <integer>
#>       [1]       II        1-2000      * |       116       NaN    II      1000
#>       [2]       II        1-2000      * |       116       NaN    II      1000
#>       [3]       II        1-2000      * |       116       NaN    II      1000
#>       [4]       II        1-2000      * |       116       NaN    II      1000
#>       [5]       II        1-2000      * |       116       NaN    II      1000
#>       ...      ...           ...    ... .       ...       ...   ...       ...
#>   [33475]       II 794001-796000      * |       513 0.0196726    II    795000
#>   [33476]       II 794001-796000      * |       513 0.0196726    II    795000
#>   [33477]       II 796001-798000      * |       514 0.0196450    II    797000
#>   [33478]       II 796001-798000      * |       514 0.0196450    II    797000
#>   [33479]       II 798001-800000      * |       515 0.0214123    II    799000
#>   -------
#>   seqinfo: 16 sequences from an unspecified genome
#> 
#> $second
#> GRanges object with 33479 ranges and 4 metadata columns:
#>           seqnames        ranges strand |    bin_id    weight   chr    center
#>              <Rle>     <IRanges>  <Rle> | <numeric> <numeric> <Rle> <integer>
#>       [1]       II        1-2000      * |       116       NaN    II      1000
#>       [2]       II     4001-6000      * |       118       NaN    II      5000
#>       [3]       II     6001-8000      * |       119       NaN    II      7000
#>       [4]       II    8001-10000      * |       120 0.0461112    II      9000
#>       [5]       II   10001-12000      * |       121 0.0334807    II     11000
#>       ...      ...           ...    ... .       ...       ...   ...       ...
#>   [33475]       II 796001-798000      * |       514 0.0196450    II    797000
#>   [33476]       II 798001-800000      * |       515 0.0214123    II    799000
#>   [33477]       II 796001-798000      * |       514 0.0196450    II    797000
#>   [33478]       II 798001-800000      * |       515 0.0214123    II    799000
#>   [33479]       II 798001-800000      * |       515 0.0214123    II    799000
#>   -------
#>   seqinfo: 16 sequences from an unspecified genome

6.2 Slot setters

6.2.1 Scores

Add any scores metric using a numerical vector.

scores(hic, 'random') <- runif(length(hic))
scores(hic)
#> List of length 3
#> names(3): count balanced random
tail(scores(hic, 'random'))
#> [1] 0.88530238 0.60790416 0.66773518 0.55711715 0.61724196 0.04565936

6.2.2 Features

Add topologicalFeatures using GRanges or Pairs.

topologicalFeatures(hic, 'viewpoints') <- GRanges("II:300001-320000")
topologicalFeatures(hic)
#> List of length 4
#> names(4): compartments borders loops viewpoints
topologicalFeatures(hic, 'viewpoints')
#> GRanges object with 1 range and 0 metadata columns:
#>       seqnames        ranges strand
#>          <Rle>     <IRanges>  <Rle>
#>   [1]       II 300001-320000      *
#>   -------
#>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

6.3 Coercing HiCExperiment

Using the as() function, HiCExperiment can be coerced in GInteractions, ContactMatrix and matrix seamlessly.

as(hic, "GInteractions")
#> GInteractions object with 33479 interactions and 5 metadata columns:
#>           seqnames1       ranges1     seqnames2       ranges2 |   bin_id1
#>               <Rle>     <IRanges>         <Rle>     <IRanges> | <numeric>
#>       [1]        II        1-2000 ---        II        1-2000 |       116
#>       [2]        II        1-2000 ---        II     4001-6000 |       116
#>       [3]        II        1-2000 ---        II     6001-8000 |       116
#>       [4]        II        1-2000 ---        II    8001-10000 |       116
#>       [5]        II        1-2000 ---        II   10001-12000 |       116
#>       ...       ...           ... ...       ...           ... .       ...
#>   [33475]        II 794001-796000 ---        II 796001-798000 |       513
#>   [33476]        II 794001-796000 ---        II 798001-800000 |       513
#>   [33477]        II 796001-798000 ---        II 796001-798000 |       514
#>   [33478]        II 796001-798000 ---        II 798001-800000 |       514
#>   [33479]        II 798001-800000 ---        II 798001-800000 |       515
#>             bin_id2     count  balanced     random
#>           <numeric> <numeric> <numeric>  <numeric>
#>       [1]       116         1       NaN 0.16846448
#>       [2]       118         2       NaN 0.18099172
#>       [3]       119         3       NaN 0.00119931
#>       [4]       120        15       NaN 0.07913460
#>       [5]       121         9       NaN 0.20917282
#>       ...       ...       ...       ...        ...
#>   [33475]       514       309 0.1194189  0.6079042
#>   [33476]       515       227 0.0956207  0.6677352
#>   [33477]       514       130 0.0501703  0.5571171
#>   [33478]       515       297 0.1249314  0.6172420
#>   [33479]       515       117 0.0536429  0.0456594
#>   -------
#>   regions: 400 ranges and 4 metadata columns
#>   seqinfo: 16 sequences from an unspecified genome
as(hic, "ContactMatrix")
#> class: ContactMatrix 
#> dim: 400 400 
#> type: dgCMatrix 
#> rownames: NULL
#> colnames: NULL
#> metadata(0):
#> regions: 400
as(hic, "matrix")[1:10, 1:10]
#> 10 x 10 sparse Matrix of class "dgTMatrix"
#>                                                                           
#>  [1,] NaN . NaN NaN        NaN        NaN        NaN        NaN        NaN
#>  [2,]   . .   .   . .          .          .          .          .         
#>  [3,] NaN .   . NaN        NaN        NaN        NaN        NaN        NaN
#>  [4,] NaN . NaN NaN        NaN        NaN        NaN        NaN        NaN
#>  [5,] NaN . NaN NaN 0.08079721 0.18680431 0.13127403 0.08833001 0.06759757
#>  [6,] NaN . NaN NaN 0.18680431 0.08183011 0.19176749 0.12687633 0.10078115
#>  [7,] NaN . NaN NaN 0.13127403 0.19176749 0.08040523 0.13690173 0.13249106
#>  [8,] NaN . NaN NaN 0.08833001 0.12687633 0.13690173 0.07977117 0.18151495
#>  [9,] NaN . NaN NaN 0.06759757 0.10078115 0.13249106 0.18151495 0.06494950
#> [10,] NaN . NaN NaN 0.06021225 0.07728955 0.09404388 0.12720548 0.11622354
#>                 
#>  [1,]        NaN
#>  [2,] .         
#>  [3,]        NaN
#>  [4,]        NaN
#>  [5,] 0.06021225
#>  [6,] 0.07728955
#>  [7,] 0.09404388
#>  [8,] 0.12720548
#>  [9,] 0.11622354
#> [10,] 0.06796588
as(hic, "data.frame")[1:10, ]
#>    seqnames1 start1 end1 width1 strand1 bin_id1 weight1 center1 seqnames2
#> 1         II      1 2000   2000       *     116     NaN    1000        II
#> 2         II      1 2000   2000       *     116     NaN    1000        II
#> 3         II      1 2000   2000       *     116     NaN    1000        II
#> 4         II      1 2000   2000       *     116     NaN    1000        II
#> 5         II      1 2000   2000       *     116     NaN    1000        II
#> 6         II      1 2000   2000       *     116     NaN    1000        II
#> 7         II      1 2000   2000       *     116     NaN    1000        II
#> 8         II      1 2000   2000       *     116     NaN    1000        II
#> 9         II      1 2000   2000       *     116     NaN    1000        II
#> 10        II      1 2000   2000       *     116     NaN    1000        II
#>    start2  end2 width2 strand2 bin_id2    weight2 center2 count balanced
#> 1       1  2000   2000       *     116        NaN    1000     1      NaN
#> 2    4001  6000   2000       *     118        NaN    5000     2      NaN
#> 3    6001  8000   2000       *     119        NaN    7000     3      NaN
#> 4    8001 10000   2000       *     120 0.04611120    9000    15      NaN
#> 5   10001 12000   2000       *     121 0.03348075   11000     9      NaN
#> 6   12001 14000   2000       *     122 0.03389168   13000     6      NaN
#> 7   14001 16000   2000       *     123 0.04164320   15000     1      NaN
#> 8   16001 18000   2000       *     124 0.01954625   17000     2      NaN
#> 9   18001 20000   2000       *     125 0.02331795   19000     6      NaN
#> 10  20001 22000   2000       *     126 0.02241734   21000     5      NaN
#>         random
#> 1  0.168464478
#> 2  0.180991724
#> 3  0.001199312
#> 4  0.079134597
#> 5  0.209172816
#> 6  0.335740399
#> 7  0.217052078
#> 8  0.129803502
#> 9  0.238773003
#> 10 0.721539329

7 Importing pairs files

Pairs files typically contain chimeric pairs (filtered after mapping), corresponding to loci that have been religated together after restriction enzyme digestion. Such files have a variety of standards.

Pairs in any of these different formats are automatically detected and imported in R with the import function:

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

8 Further documentation

Please check ?HiCExperiment in R for a full description of available slots, getters and setters, and comprehensive examples of interaction with a HiCExperiment object.

9 Session info

sessionInfo()
#> R Under development (unstable) (2023-10-22 r85388)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] HiCExperiment_1.3.0  HiContactsData_1.3.0 ExperimentHub_2.11.0
#>  [4] AnnotationHub_3.11.0 BiocFileCache_2.11.0 dbplyr_2.3.4        
#>  [7] GenomicRanges_1.55.0 GenomeInfoDb_1.39.0  IRanges_2.37.0      
#> [10] S4Vectors_0.41.0     BiocGenerics_0.49.0  dplyr_1.1.3         
#> [13] BiocStyle_2.31.0    
#> 
#> loaded via a namespace (and not attached):
#>   [1] DBI_1.1.3                     bitops_1.0-7                 
#>   [3] rlang_1.1.1                   magrittr_2.0.3               
#>   [5] matrixStats_1.0.0             compiler_4.4.0               
#>   [7] RSQLite_2.3.1                 png_0.1-8                    
#>   [9] vctrs_0.6.4                   stringr_1.5.0                
#>  [11] pkgconfig_2.0.3               crayon_1.5.2                 
#>  [13] fastmap_1.1.1                 XVector_0.43.0               
#>  [15] ellipsis_0.3.2                utf8_1.2.4                   
#>  [17] promises_1.2.1                rmarkdown_2.25               
#>  [19] tzdb_0.4.0                    ggbeeswarm_0.7.2             
#>  [21] strawr_0.0.91                 purrr_1.0.2                  
#>  [23] bit_4.0.5                     xfun_0.40                    
#>  [25] zlibbioc_1.49.0               cachem_1.0.8                 
#>  [27] jsonlite_1.8.7                blob_1.2.4                   
#>  [29] later_1.3.1                   rhdf5filters_1.15.0          
#>  [31] DelayedArray_0.29.0           Rhdf5lib_1.25.0              
#>  [33] BiocParallel_1.37.0           interactiveDisplayBase_1.41.0
#>  [35] parallel_4.4.0                R6_2.5.1                     
#>  [37] bslib_0.5.1                   stringi_1.7.12               
#>  [39] jquerylib_0.1.4               Rcpp_1.0.11                  
#>  [41] bookdown_0.36                 SummarizedExperiment_1.33.0  
#>  [43] knitr_1.44                    readr_2.1.4                  
#>  [45] httpuv_1.6.12                 Matrix_1.6-1.1               
#>  [47] tidyselect_1.2.0              abind_1.4-5                  
#>  [49] yaml_2.3.7                    codetools_0.2-19             
#>  [51] curl_5.1.0                    lattice_0.22-5               
#>  [53] tibble_3.2.1                  InteractionSet_1.31.0        
#>  [55] Biobase_2.63.0                shiny_1.7.5.1                
#>  [57] withr_2.5.1                   KEGGREST_1.43.0              
#>  [59] evaluate_0.22                 ggrastr_1.0.2                
#>  [61] archive_1.1.6                 Biostrings_2.71.0            
#>  [63] pillar_1.9.0                  BiocManager_1.30.22          
#>  [65] filelock_1.0.2                MatrixGenerics_1.15.0        
#>  [67] generics_0.1.3                vroom_1.6.4                  
#>  [69] RCurl_1.98-1.12               hms_1.1.3                    
#>  [71] ggplot2_3.4.4                 BiocVersion_3.19.0           
#>  [73] munsell_0.5.0                 scales_1.2.1                 
#>  [75] xtable_1.8-4                  glue_1.6.2                   
#>  [77] tools_4.4.0                   BiocIO_1.13.0                
#>  [79] RSpectra_0.16-1               rhdf5_2.47.0                 
#>  [81] grid_4.4.0                    tidyr_1.3.0                  
#>  [83] colorspace_2.1-0              AnnotationDbi_1.65.0         
#>  [85] GenomeInfoDbData_1.2.11       beeswarm_0.4.0               
#>  [87] vipor_0.4.5                   cli_3.6.1                    
#>  [89] rappdirs_0.3.3                fansi_1.0.5                  
#>  [91] S4Arrays_1.3.0                gtable_0.3.4                 
#>  [93] HiContacts_1.5.0              sass_0.4.7                   
#>  [95] digest_0.6.33                 SparseArray_1.3.0            
#>  [97] memoise_2.0.1                 htmltools_0.5.6.1            
#>  [99] lifecycle_1.0.3               httr_1.4.7                   
#> [101] mime_0.12                     bit64_4.0.5