<!-- README.md is generated from README.Rmd. Please edit that file --> # HiCParser <!-- badges: start --> [](https://github.com/emaigne/HiCParser/issues) [](https://github.com/emaigne/HiCParser/pulls) [](https://lifecycle.r-lib.org/articles/stages.html#experimental) [](https://bioconductor.org/checkResults/release/bioc-LATEST/HiCParser) [](https://bioconductor.org/checkResults/devel/bioc-LATEST/HiCParser) [](http://bioconductor.org/packages/stats/bioc/HiCParser/) [](https://support.bioconductor.org/tag/HiCParser) [](https://bioconductor.org/packages/release/bioc/html/HiCParser.html#since) [](http://bioconductor.org/checkResults/devel/bioc-LATEST/HiCParser/) [](https://bioconductor.org/packages/release/bioc/html/HiCParser.html#since) [](https://github.com/emaigne/HiCParser/actions/workflows/check-bioc.yml) [](https://app.codecov.io/gh/emaigne/HiCParser?branch=devel) <!-- badges: end --> The goal of `HiCParser` is to parse Hi-C data (`HiCParser` supports serveral formats), and import them in R, as an `InteractionSet` object. ## Installation instructions Get the latest stable `R` release from [CRAN](http://cran.r-project.org/). Then install `HiCParser` from [Bioconductor](http://bioconductor.org/) using the following code: ``` r if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("HiCParser") ``` And the development version from [GitHub](https://github.com/emaigne/HiCParser) with: ``` r BiocManager::install("emaigne/HiCParser") ``` Then load the package : ``` r library("HiCParser") ``` ## Supported formats So far, `HiCParser` supports: - [cool and mcool](https://github.com/open2c/cooler) formats - [hic](https://github.com/aidenlab/hictools) format - [HiC-Pro](https://github.com/nservant/HiC-Pro) format - A tabular format, where - the first column is named “chromosome” - the second column is named “position 1” or “position.1” - the third column is named “position 2” or “position.2” - the fourth column is named “*x*.R*y*”, and *x* is the id of the condition (“1”, or “2”, usually), *y* is the id of the replicate (“1”, “2”, “3”, etc.); it should contain matrix counts - the remaining columns are optional, and should be formatted like the fourth column ## Example ### hic format We show here how to parse one hic format file. ``` r hicFilePath <- system.file("extdata", "hicsample_21.hic", package = "HiCParser") data <- parseHiC( paths = hicFilePath, binSize = 5000000, conditions = 1, replicates = 1 ) ``` Note that a hic file can include several matrices, with different bin sizes. This is why the bin size should be provided. We show here how to parse several files (actually, the same file, several times). We suppose here that we have 2 conditions, with 3 replicates for each condition. ``` r data <- parseHiC( paths = rep(hicFilePath, 6), binSize = 5000000, conditions = rep(seq(2), each = 3), replicates = rep(seq(3), 2) ) ``` Currently, `HiCParser` supports the hic format up to the version 9. ### HiC-Pro format A HiC-Pro file contains a matrix file, and a bed file. A different bed file could be use for each matrix file, but the same can also be used. ``` r matrixFilePath <- system.file("extdata", "hicsample_21.matrix", package = "HiCParser") bedFilePath <- system.file("extdata", "hicsample_21.bed", package = "HiCParser") data <- parseHiCPro( matrixPaths = rep(matrixFilePath, 6), bedPaths = bedFilePath, conditions = rep(seq(2), each = 3), replicates = rep(seq(3), 2) ) ``` ### cool and mcool formats Please note that the cool and mcool format store data in HDF5 format. The [HDF5 package](https://bioconductor.org/packages/release/bioc/html/rhdf5.html) is not included by default, because it requires a substantial time to be compiled, and many users will not need the cool/mcool parser. So, in order to use the cool/mcool parser, you should install the `rhdf5` package. The cool format include only one bin size. ``` r if (!"rhdf5" %in% installed.packages()) { install.packages("rhdf5") } coolFilePath <- system.file("extdata", "hicsample_21.cool", package = "HiCParser" ) data <- parseCool( paths = rep(coolFilePath, 6), conditions = rep(seq(2), each = 3), replicates = rep(seq(3), 2) ) ``` The mcool format may include several bin sizes. It is thus compulsory to mention it. The same function is used for the cool/mcool formats. ``` r mcoolFilePath <- system.file("extdata", "hicsample_21.mcool", package = "HiCParser" ) data <- parseCool( paths = rep(mcoolFilePath, 6), binSize = 5000000, conditions = rep(seq(2), each = 3), replicates = rep(seq(3), 2) ) ``` ## Tabular files A tabular file is a tab-separated multi-replicate sparse matrix with a header: chromosome position 1 position 2 C1.R1 C1.R2 C1.R3 ... Y 1500000 7500000 145 184 72 ... The number of interactions between `position 1` and `position 2` of `chromosome` are reported in each `condition.replicate` column. There is no limit to the number of conditions and replicates. To load Hi-C data in this format: ``` r hic.experiment <- parseTabular( system.file("extdata", "hicsample_21.tsv", package = "HiCParser" ), sep = "\t" ) ``` ### Output The output is a [InteractionSet](https://bioconductor.org/packages/release/bioc/html/InteractionSet.html). This object can store one or several samples. Please read the [corresponding vignette](https://bioconductor.org/packages/devel/bioc/vignettes/InteractionSet/inst/doc/interactions.html) in order to known more about this format. ``` r library("HiCParser") hicFilePath <- system.file("extdata", "hicsample_21.hic", package = "HiCParser") hic.experiment <- parseHiC( paths = rep(hicFilePath, 6), binSize = 5000000, conditions = rep(seq(2), each = 3), replicates = rep(seq(3), 2) ) #> #> Parsing '/tmp/RtmpHFfOT6/temp_libpathc52a1c587e54/HiCParser/extdata/hicsample_21.hic'. #> #> Parsing '/tmp/RtmpHFfOT6/temp_libpathc52a1c587e54/HiCParser/extdata/hicsample_21.hic'. #> #> Parsing '/tmp/RtmpHFfOT6/temp_libpathc52a1c587e54/HiCParser/extdata/hicsample_21.hic'. #> #> Parsing '/tmp/RtmpHFfOT6/temp_libpathc52a1c587e54/HiCParser/extdata/hicsample_21.hic'. #> #> Parsing '/tmp/RtmpHFfOT6/temp_libpathc52a1c587e54/HiCParser/extdata/hicsample_21.hic'. #> #> Parsing '/tmp/RtmpHFfOT6/temp_libpathc52a1c587e54/HiCParser/extdata/hicsample_21.hic'. hic.experiment #> class: InteractionSet #> dim: 44 6 #> metadata(0): #> assays(1): '' #> rownames: NULL #> rowData names(1): chromosome #> colnames: NULL #> colData names(2): condition replicate #> type: StrictGInteractions #> regions: 9 ``` The conditions and replicates are reported in the `colData` slot : ``` r SummarizedExperiment::colData(hic.experiment) #> DataFrame with 6 rows and 2 columns #> condition replicate #> <integer> <integer> #> 1 1 1 #> 2 1 2 #> 3 1 3 #> 4 2 1 #> 5 2 2 #> 6 2 3 ``` They corresponds to columns of the `assays` matrix (containing interactions values): ``` r head(SummarizedExperiment::assay(hic.experiment)) #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] 79 79 79 79 79 79 #> [2,] 22 22 22 22 22 22 #> [3,] 3 3 3 3 3 3 #> [4,] 1 1 1 1 1 1 #> [5,] 1 1 1 1 1 1 #> [6,] 2 2 2 2 2 2 ``` The positions of interactions are in the `interactions` slot of the object: ``` r InteractionSet::interactions(hic.experiment) #> StrictGInteractions object with 44 interactions and 1 metadata column: #> seqnames1 ranges1 seqnames2 ranges2 | chromosome #> <Rle> <IRanges> <Rle> <IRanges> | <Rle> #> [1] 21 5000001-10000000 --- 21 5000001-10000000 | 21 #> [2] 21 5000001-10000000 --- 21 10000001-15000000 | 21 #> [3] 21 5000001-10000000 --- 21 15000001-20000000 | 21 #> [4] 21 5000001-10000000 --- 21 20000001-25000000 | 21 #> [5] 21 5000001-10000000 --- 21 25000001-30000000 | 21 #> ... ... ... ... ... ... . ... #> [40] 21 35000001-40000000 --- 21 40000001-45000000 | 21 #> [41] 21 35000001-40000000 --- 21 45000001-50000000 | 21 #> [42] 21 40000001-45000000 --- 21 40000001-45000000 | 21 #> [43] 21 40000001-45000000 --- 21 45000001-50000000 | 21 #> [44] 21 45000001-50000000 --- 21 45000001-50000000 | 21 #> ------- #> regions: 9 ranges and 1 metadata column #> seqinfo: 1 sequence from an unspecified genome; no seqlengths ``` ## Citation Below is the citation output from using `citation('HiCParser')` in R. Please run this yourself to check for any updates on how to cite **HiCParser**. To cite the ‘HiCParser’ HiCParser in a publication, use : Maigné E, Zytnicki M (2024). *A multiple format Hi-C data parser*. <doi:10.18129/B9.bioc.HiCParser> <https://doi.org/10.18129/B9.bioc.HiCParser>, <https://github.com/emaigne/HiCParser/HiCParser> - R package version 0.1.0, <http://www.bioconductor.org/packages/HiCParser>. As a BibTeX entry : @Manual{hicparser, title = {A multiple format Hi-C data parser}, author = {Elise Maigné and Matthias Zytnicki}, year = {2024}, url = {http://www.bioconductor.org/packages/HiCParser}, note = {https://github.com/emaigne/HiCParser/HiCParser - R package version 0.1.0}, doi = {10.18129/B9.bioc.HiCParser}, } Please note that the `HiCParser` was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package. ## Code of Conduct Please note that the `HiCParser` project is released with a [Contributor Code of Conduct](http://bioconductor.org/about/code-of-conduct/). By contributing to this project, you agree to abide by its terms. ## Development tools - Continuous code testing is possible thanks to [GitHub actions](https://www.tidyverse.org/blog/2020/04/usethis-1-6-0/) through *[usethis](https://CRAN.R-project.org/package=usethis)*, *[remotes](https://CRAN.R-project.org/package=remotes)*, and *[rcmdcheck](https://CRAN.R-project.org/package=rcmdcheck)* customized to use [Bioconductor’s docker containers](https://www.bioconductor.org/help/docker/) and *[BiocCheck](https://bioconductor.org/packages/3.17/BiocCheck)*. - Code coverage assessment is possible thanks to [codecov](https://codecov.io/gh) and *[covr](https://CRAN.R-project.org/package=covr)*. - The [documentation website](http://emaigne.github.io/HiCParser) is automatically updated thanks to *[pkgdown](https://CRAN.R-project.org/package=pkgdown)*. - The code is styled automatically thanks to *[styler](https://CRAN.R-project.org/package=styler)*. - The documentation is formatted thanks to *[devtools](https://CRAN.R-project.org/package=devtools)* and *[roxygen2](https://CRAN.R-project.org/package=roxygen2)*. For more details, check the `dev` directory. This package was developed using *[biocthis](https://bioconductor.org/packages/3.17/biocthis)*.