Clustering of Hi-C contact maps
Shubham Chaturvedi, Pierre Neuvial, Nathalie Vialaneix
2022-08-18
# IMPORTANT: this vignette can not be created if HiTC is not installed
if (!require("HiTC", quietly = TRUE)) {
knitr::opts_chunk$set(eval = FALSE)
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## I, expand.grid, unnameIntroduction
Hi-C is a sequencing-based molecular assay designed to measure intra and inter-chromosomal interactions between the DNA molecule. In particular, the identification of Topologically-Associated Domains (TADs), that is, of regions of the genome in which physical interactions are frequent, provides insight into the three-dimensional organization of a genome [2].
Hi-C data are in the form of two-dimensional contact maps,
i.e., matrices whose \(i,j\)
entry quantifies the intensity of the physical interaction between two
genome regions \(i\) and \(j\) at the DNA level. In this vignette, we
demonstrate the use of adjclust::hicClust to perform
adjacency-constrained hierarchical agglomerative clustering (HAC) of
Hi-C contact maps. The output of this function is a dendrogram, which
can be cut to identify TADs. The algorithm used for
adjacency-constrained (HAC) is described in [3,4].
library("adjclust")Loading and displaying a sample Hi-C contact map
The data set hic_imr90_40_XX is an object of class
HTCexp which has been obtained from the HiTC
package [4]. It is a contact map corresponding to the first 500 x 500
bins on chromosome X vs chromosome X.
load(system.file("extdata", "hic_imr90_40_XX.rda", package = "adjclust"))The script used to create this map can be found by executing the following command:
system.file("system/create_hic_chrXchrX.R", package="adjclust")## [1] "/tmp/RtmpVQ10FC/Rinst90df3efd4959/adjclust/system/create_hic_chrXchrX.R"Now we have a look at the data.
HiTC::mapC(hic_imr90_40_XX)
Using hicClust
hicClust operates directly on objects of class
HTCexp
fit <- hicClust(hic_imr90_40_XX)It is also possible to work on binned data. Below we choose a bin size of \(5 \times 10^5\):
binned <- HiTC::binningC(hic_imr90_40_XX, binsize = 1e5)
fitB <- hicClust(binned)
fitB##
## Call:
## run.adjclust(mat = mat, type = type, h = h, strictCheck = strictCheck)
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## Cluster method : hicClust
## Number of objects: 205HiTC::mapC(binned)
The output is of class chac. In particular, it can be
plotted as a dendrogram silently relying on the function
plot.dendrogram:
plot(fitB, mode = "corrected")
Moreover, the output contains an element named merge
which describes the successive merges of the clustering, and an element
gains which gives the improvement in the criterion
optimized by the clustering at each successive merge.
head(cbind(fitB$merge, fitB$gains))## [,1] [,2]
## [1,] -3 -4
## [2,] -2 1
## [3,] 2 -5
## [4,] -1 3
## [5,] 4 -6
## [6,] -17 -18Other types of input
Contacts maps can also be stored as objects of class
Matrix::dsCMatrix, or as plain text files. These types of
input are also accepted as first argument to hicClust.
References
[1] Ambroise C., Dehman A., Neuvial P., Rigaill G., and Vialaneix N. (2019). Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. Algorithms for Molecular Biology, 14, 22.
[2] Dixon J.R., et al (2012). Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 485(7398), 376.
[3] Randriamihamison N., Vialaneix N., and Neuvial P. (2021). Applicability and interpretability of Ward’s hierarchical agglomerative clustering with or without contiguity constraints. Journal of Classification, 38, 363–389.
[4] Servant N., et al (2012). HiTC: Exploration of High-Throughput ‘C’ experiments. Bioinformatics, 28(21), 2843-2844.
Session information
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
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## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] adjclust_0.6.5 HiTC_1.40.0 GenomicRanges_1.48.0
## [4] GenomeInfoDb_1.32.3 IRanges_2.30.0 S4Vectors_0.34.0
## [7] BiocGenerics_0.42.0
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## loaded via a namespace (and not attached):
## [1] SummarizedExperiment_1.26.1 capushe_1.1.1
## [3] xfun_0.31 bslib_0.3.1
## [5] lattice_0.20-45 htmltools_0.5.2
## [7] rtracklayer_1.56.1 yaml_2.3.5
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## [11] jquerylib_0.1.4 BiocParallel_1.30.3
## [13] RColorBrewer_1.1-3 matrixStats_0.62.0
## [15] GenomeInfoDbData_1.2.8 stringr_1.4.0
## [17] zlibbioc_1.42.0 MatrixGenerics_1.8.1
## [19] Biostrings_2.64.0 codetools_0.2-18
## [21] evaluate_0.15 restfulr_0.0.15
## [23] Biobase_2.56.0 knitr_1.39
## [25] fastmap_1.1.0 parallel_4.2.1
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## [31] XVector_0.36.0 Rsamtools_2.12.0
## [33] rjson_0.2.21 digest_0.6.29
## [35] stringi_1.7.8 BiocIO_1.6.0
## [37] grid_4.2.1 cli_3.3.0
## [39] tools_4.2.1 bitops_1.0-7
## [41] magrittr_2.0.3 sass_0.4.1
## [43] RCurl_1.98-1.7 crayon_1.5.1
## [45] MASS_7.3-58 Matrix_1.4-2
## [47] sparseMatrixStats_1.8.0 rmarkdown_2.14
## [49] rstudioapi_0.13 R6_2.5.1
## [51] GenomicAlignments_1.32.1 compiler_4.2.1