GitHub - js2264/HiContacts: Analyzing Hi-C data in R with HiCExperiment objects (original) (raw)

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HiContacts

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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.

DOI

HiContacts provides tools to investigate (m)cool matrices imported in R by HiCExperiment.

It leverages the HiCExperiment class of objects, built on pre-existing Bioconductor objects, namely InteractionSet, GInterations and ContactMatrix (Lun, Perry & Ing-Simmons, F1000Research 2016), and provides analytical and visualization tools to investigate contact maps.

Installation

HiContacts is available in Bioconductor. To install the current release, use:

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("HiContacts")

To install the most recent version of HiContacts, you can use:

install.packages("devtools") devtools::install_github("js2264/HiContacts") library(HiContacts)

Citation

If you are using HiContacts in your research, please cite:

Serizay J (2022). HiContacts: HiContacts: R interface to cool files. R package version 1.1.0https://github.com/js2264/HiContacts.

How to use HiContacts

HiContacts includes a introduction vignette where its usage is illustrated. To access the vignette, please use:

Visualising Hi-C contact maps and features

Importing a Hi-C contact maps file with HiCExperiment

mcool_file <- HiContactsData::HiContactsData('yeast_wt', format = 'mcool') range <- 'I:20000-80000' # range of interest availableResolutions(mcool_file) hic <- HiCExperiment::import(mcool_file, format = 'mcool', focus = range, resolution = 1000) hic

Plotting matrices (square or horizontal)

plotMatrix(hic, use.scores = 'count') plotMatrix(hic, use.scores = 'balanced', limits = c(-4, -1)) plotMatrix(hic, use.scores = 'balanced', limits = c(-4, -1), maxDistance = 100000)

Plotting matrices with topological features

library(rtracklayer) mcool_file <- HiContactsData::HiContactsData('yeast_wt', format = 'mcool') hic <- import(mcool_file, format = 'mcool', focus = 'IV') loops <- system.file("extdata", 'S288C-loops.bedpe', package = 'HiContacts') |> import() |> InteractionSet::makeGInteractionsFromGRangesPairs() borders <- system.file("extdata", 'S288C-borders.bed', package = 'HiContacts') |> import() p <- plotMatrix( hic, loops = loops, borders = borders, limits = c(-4, -1), dpi = 120 )

Plotting aggregated matrices (a.k.a. APA plots)

contacts <- contacts_yeast() contacts <- zoom(contacts, resolution = 2000) aggr_centros <- aggregate(contacts, targets = topologicalFeatures(contacts, 'centromeres')) plotMatrix(aggr_centros, use.scores = 'detrended', limits = c(-1, 1), scale = 'linear')

Mapping topological features

Chromosome compartments

microC_mcool <- fourDNData::fourDNData('4DNES14CNC1I', 'mcool') hic <- import(microC_mcool, format = 'mcool', resolution = 10000000) genome <- BSgenome.Mmusculus.UCSC.mm10::BSgenome.Mmusculus.UCSC.mm10

- Get compartments

hic <- getCompartments( hic, resolution = 100000, genome = genome, chromosomes = c('chr17', 'chr19') )

- Export compartments as bigwig and bed files

export(IRanges::coverage(metadata(hic)$eigens, weight = 'eigen'), 'microC_compartments.bw') export( topologicalFeatures(hic, 'compartments')[topologicalFeatures(hic, 'compartments')$compartment == 'A'], 'microC_A-compartments.bed' ) export( topologicalFeatures(hic, 'compartments')[topologicalFeatures(hic, 'compartments')$compartment == 'B'], 'microC_B-compartments.bed' )

- Generate saddle plot

plotSaddle(hic)

Diamond insulation score and chromatin domains borders

- Compute insulation score

hic <- refocus(hic, 'chr19:1-30000000') |> zoom(resolution = 10000) |> getDiamondInsulation(window_size = 100000) |> getBorders()

- Export insulation as bigwig track and borders as bed file

export(IRanges::coverage(metadata(hic)$insulation, weight = 'insulation'), 'microC_insulation.bw') export(topologicalFeatures(hic, 'borders'), 'microC_borders.bed')

In-depth analysis of HiCExperiment objects

Arithmetics

Detrend

Autocorrelate

Divide

Merge

Distance law, a.k.a. P(s)

hic <- import(CoolFile( mcool_file, pairs = HiContactsData::HiContactsData('yeast_wt', format = 'pairs.gz') )) ps <- distanceLaw(hic) plotPs(ps, ggplot2::aes(x = binned_distance, y = norm_p))

Virtual 4C

hic <- import(CoolFile(mcool_file)) v4C <- virtual4C(hic, viewpoint = GRanges('V:150000-170000')) plot4C(v4C)

Cis-trans ratios

hic <- import(CoolFile(mcool_file)) cisTransRatio(hic)

Scalograms

HiCExperiment ecosystem

HiCool is integrated within the HiCExperiment ecosystem in Bioconductor. Read more about the HiCExperiment class and handling Hi-C data in Rhere.