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[doi:10.1038/s41467-024-44761-x](https://doi.org/10.1038/s41467-024-44761-x). [](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 > <https://github.com/js2264/HiContacts>. ## 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).  - [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