Genomic2DTK - Quick start • Genomic2DTK

Quick start from the import of HiC data to the aggregation of HiC contact in 3D. It includes 4 steps:

Import HiC
Import genomic coordinates
Submatrices extractions
Plot and visualization

Requirements

Installation

remotes::install_github("NChanard/Genomic2DTK")

Load library

library(Genomic2DTK)

Test dataset

Description

Data were obtained from Drosophila melanogaster S2 cells. HiC test dataset: Directly downloaded from the 4DN platform. Genomic coordinates: ChIPseq peaks of Beaf-32 protein in wild type cells (GSM1278639).

Genomic 3D structure

For a test, please download HiC data in .hic format (Juicer).

options(timeout = 3600) 
temp.dir <- file.path(tempdir(), "HIC_DATA")
dir.create(temp.dir)

Hic.url <- "https://4dn-open-data-public.s3.amazonaws.com/fourfront-webprod/wfoutput/7386f953-8da9-47b0-acb2-931cba810544/4DNFIOTPSS3L.hic"
HicOutput.pth <- file.path(temp.dir, "Control_HIC.hic")
download.file(Hic.url, HicOutput.pth, method = 'curl', extra = '-k')

Genomic location and annotation data

These kind of data can be imported in R with rtracklayer package.

data("Beaf32_Peaks.gnr")

View

seq	start	end	strand	name	score
2L	35594	35725	*	Beaf32_2	76
2L	47296	47470	*	Beaf32_3	44
2L	65770	65971	*	Beaf32_5	520

Additional genome informations

Required genomic informations used by the functions during the entire pipeline are a data.frame containing chromosomes names and sized and the binSize, corresponding to the HiC matrices at the same resolution.

seqlengths.num <- c('2L'=23513712, '2R'=25286936)
chromSize.dtf  <- data.frame(
    seqnames   = names(seqlengths.num ), 
    seqlengths = seqlengths.num
    )
binSize.num <- 1000

1 Import HiC

The package supports the import and normalization of HiC data.

Import

Genomic2DTK can import HiC data stored in the main formats: .hic, .cool, .mcool, .h5, .hdf5. The pacakage imports only the raw counts in R. Therefore it is necessary to perform the balancing and observed/expected correction steps.

hic.cmx_lst <- ImportHiC(
    file.pth      = HicOutput.pth,
    res.num       = binSize.num,
    chrom_1.chr   = c("2L", "2R")
    )

Balancing

hic.cmx_lst <- BalanceHiC(hic.cmx_lst)
#> No trans matrix, Normalisation won't be applied on trans matrices

Observed/Expected Correction

hic.cmx_lst <- OverExpectedHiC(hic.cmx_lst)

2 Import genomic coordinates

Genomic coordinates data (ChIP seq peaks or any other feature) need to be indexed using the same referenced genome as for HiC data. Then the genomic coordinates are paired in GInteraction objects.

Features Indexing

Beaf_Index.gnr <- IndexFeatures(
    gRange.gnr_lst        = list(Beaf = Beaf32_Peaks.gnr),
    chromSize.dtf         = chromSize.dtf,
    binSize.num           = binSize.num
    )

Beaf32 <-> Beaf32 putatives

Constraints for the distance between interaction sites are defined here in order to limit the number of pairs.

Beaf_Pairs.gni <- SearchPairs(
    indexAnchor.gnr = Beaf_Index.gnr,
    minDist.num     = "25KB",
    maxDist.num     = "100KB"
    )

3 Submatrices extractions

Once data have been imported, interactions are extracted out of the pairs of genomic coordinates.

Extraction

Beaf.mtx_lst <- ExtractSubmatrix(
    feature.gn  = Beaf_Pairs.gni,
    hic.cmx_lst = hic.cmx_lst
    )

4 Plot and visualization

Submatrices are aggregated as sum, average or median. Then, aggregated matrix is plotted as a heatmap of contact frequencies (in the example, contacts surounding Beaf-32 sites).

Aggregation

aggreg.mtx <- Aggregation(Beaf.mtx_lst)

Visulatisation

ggAPA(
    apa.mtx = aggreg.mtx,
    title.chr = "APA Beaf <-> Beaf"
    )

Session Info

sessionInfo()
#> R version 4.2.2 (2022-10-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.1 LTS
#> 
#> 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
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] Genomic2DTK_0.99.0          InteractionSet_1.24.0      
#>  [3] SummarizedExperiment_1.26.1 Biobase_2.56.0             
#>  [5] MatrixGenerics_1.8.1        matrixStats_0.63.0         
#>  [7] GenomicRanges_1.48.0        GenomeInfoDb_1.32.4        
#>  [9] IRanges_2.30.1              S4Vectors_0.34.0           
#> [11] BiocGenerics_0.42.0        
#> 
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.9             lattice_0.20-45        tidyr_1.2.1           
#>  [4] rprojroot_2.0.3        digest_0.6.30          utf8_1.2.2            
#>  [7] R6_2.5.1               evaluate_0.18          ggplot2_3.4.0         
#> [10] highr_0.9              pillar_1.8.1           zlibbioc_1.42.0       
#> [13] rlang_1.0.6            jquerylib_0.1.4        Matrix_1.5-1          
#> [16] rmarkdown_2.18         pkgdown_2.0.6          labeling_0.4.2        
#> [19] textshaping_0.3.6      desc_1.4.2             BiocParallel_1.30.4   
#> [22] stringr_1.5.0          munsell_0.5.0          RCurl_1.98-1.9        
#> [25] DelayedArray_0.22.0    compiler_4.2.2         xfun_0.35             
#> [28] pkgconfig_2.0.3        systemfonts_1.0.4      strawr_0.0.9          
#> [31] htmltools_0.5.4        tidyselect_1.2.0       tibble_3.1.8          
#> [34] GenomeInfoDbData_1.2.8 codetools_0.2-18       fansi_1.0.3           
#> [37] dplyr_1.0.10           withr_2.5.0            bitops_1.0-7          
#> [40] grid_4.2.2             gtable_0.3.1           jsonlite_1.8.4        
#> [43] lifecycle_1.0.3        magrittr_2.0.3         scales_1.2.1          
#> [46] cli_3.4.1              stringi_1.7.8          cachem_1.0.6          
#> [49] farver_2.1.1           XVector_0.36.0         fs_1.5.2              
#> [52] bslib_0.4.1            ellipsis_0.3.2         ragg_1.2.4            
#> [55] generics_0.1.3         vctrs_0.5.1            tools_4.2.2           
#> [58] glue_1.6.2             purrr_0.3.5            parallel_4.2.2        
#> [61] fastmap_1.1.0          yaml_2.3.6             colorspace_2.0-3      
#> [64] memoise_2.0.1          knitr_1.41             sass_0.4.4

Genomic2DTK - Quick start

Nicolas Chanard

2022-12-08