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# HiContacts \[👉 OHCA book 📖\](https://js2264.github.io/OHCA/) \*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\. \[doi:10.1038/s41467-024-44761-x\](https://doi.org/10.1038/s41467-024-44761-x). \[!\[DOI\](https://img.shields.io/badge/Nat.\_Commun-10.1038/s41467--024--44761--x-blue)\](https://www.nature.com/articles/s41467-024-44761-x) --- 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: \`\`\`r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("HiContacts") \`\`\` To install the most recent version of \`HiContacts\`, you can use: \`\`\`r 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 1.1.0 > . ## How to use \`HiContacts\` \`HiContacts\` includes a introduction vignette where its usage is illustrated. To access the vignette, please use: \`\`\`r vignette('HiContacts') \`\`\` ## Visualising Hi-C contact maps and features ### Importing a Hi-C contact maps file with \`HiCExperiment\` \`\`\`r 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) \`\`\`r 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 \`\`\`r 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) \`\`\`r 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 \`\`\`r 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 \`\`\`r # - 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) \`\`\`r 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 \`\`\`r hic <- import(CoolFile(mcool\_file)) v4C <- virtual4C(hic, viewpoint = GRanges('V:150000-170000')) plot4C(v4C) \`\`\` ### Cis-trans ratios \`\`\`r hic <- import(CoolFile(mcool\_file)) cisTransRatio(hic) \`\`\` ### Scalograms \`\`\`r \`\`\` ## HiCExperiment ecosystem \`HiCool\` is integrated within the \`HiCExperiment\` ecosystem in Bioconductor. Read more about the \`HiCExperiment\` class and handling Hi-C data in R \[here\](https://github.com/js2264/HiCExperiment). !\[\](https://raw.githubusercontent.com/js2264/HiCExperiment/devel/man/figures/HiCExperiment\_ecosystem.png) - \[HiCExperiment\](https://github.com/js2264/HiCExperiment): Parsing Hi-C files in R - \[HiCool\](https://github.com/js2264/HiCool): End-to-end integrated workflow to process fastq files into .cool and .pairs files - \[HiContacts\](https://github.com/js2264/HiContacts): Investigating Hi-C results in R - \[HiContactsData\](https://github.com/js2264/HiContactsData): Data companion package - \[fourDNData\](https://github.com/js2264/fourDNData): Gateway package to 4DN-hosted Hi-C experiments