HiContactsData is a companion data package giving programmatic access to
several processed Hi-C files for demonstration, such as cool, mcool and
pairs files. It is meant to be used with HiContacts
.
library(HiContactsData)
The only function provided by HiContactsData package is HiContactsData()
.
Several files are available using this function, namely:
sample
: yeast_wt
, format
= fastq_R{12}
)sample
: yeast_wt
, format
= HiCool_log
)sample
: yeast_wt
, format
= cool
)sample
: yeast_wt
, format
= mcool
)sample
: yeast_g1
, format
= mcool
)sample
: yeast_g1
, format
= mcool
)sample
: yeast_g2m
, format
= pairs
)sample
: yeast_g2m
, format
= pairs
)sample
: yeast_wt
, format
= hic
)sample
: yeast_wt
, format
= hicpro_matrix
)sample
: yeast_wt
, format
= hicpro_bed
)sample
: yeast_wt
, format
= pairs
)sample
: yeast_Eco1
, format
= mcool
)sample
: yeast_Eco1
, format
= pairs
)sample
: mESCs
, format
= mcool
)sample
: mESCs
, format
= pairs
)sample
: microC
, format
= mcool
)Yeast data comes from Bastie, Chapard et al., Nature Structural & Molecular Biology 2022 and mouse ESC data comes from Bonev et al., Cell 2017. Human HcFF6 micro-C data comes from Krietenstein et al., Mol. Cell 2020.
To download one of these files, one can specify a sample
and a file format
:
cool_file <- HiContactsData()
#> Available files:
#> sample format genome condition
#> 1 yeast_wt fastq_R1 S288C wild-type
#> 2 yeast_wt fastq_R2 S288C wild-type
#> 3 yeast_wt HiCool_log S288C wild-type
#> 4 yeast_wt pairs.gz S288C wild-type
#> 5 yeast_wt cool S288C wild-type
#> 6 yeast_wt mcool S288C wild-type
#> 7 yeast_g1 mcool S288C wild-type
#> 8 yeast_g1 pairs S288C wild-type
#> 9 yeast_g2m mcool S288C wild-type
#> 10 yeast_g2m pairs S288C wild-type
#> 11 yeast_wt hic S288C wild-type
#> 12 yeast_wt hicpro_matrix S288C wild-type
#> 13 yeast_wt hicpro_bed S288C wild-type
#> 14 yeast_wt hicpro_pairs S288C wild-type
#> 15 yeast_eco1 mcool S288C Eco1-AID+IAA
#> 16 yeast_eco1 pairs.gz S288C Eco1-AID+IAA
#> 17 mESCs mcool mm10 mESCs
#> 18 mESCs pairs.gz mm10 mESCs
#> 19 microC mcool GRCh38 HFFc6
#> notes EHID
#> 1 fastq (R1) EH7783
#> 2 fastq (R2) EH7784
#> 3 HiCool log file EH7785
#> 4 only pairs from chrII are provided EH7703
#> 5 .cool file @ resolution of 1kb EH7701
#> 6 multi-res .mcool file EH7702
#> 7 multi-res .mcool file EH8562
#> 8 filtered pairs are provided EH8564
#> 9 multi-res .mcool file EH8563
#> 10 filtered pairs are provided EH8565
#> 11 multi-res .hic file EH7786
#> 12 HiC-Pro matrix file @ 1kb EH7787
#> 13 HiC-Pro bed file @ 1kb EH7788
#> 14 HiC-Pro .allValidPairs file EH7789
#> 15 multi-res .mcool file EH7704
#> 16 only pairs from chrII are provided EH7705
#> 17 multi-res .mcool file EH7706
#> 18 only pairs from chr13 are provided EH7707
#> 19 multi-res .mcool file, only chr17 is provided EH8535
#>
cool_file <- HiContactsData(sample = 'yeast_wt', format = 'cool')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
cool_file
#> EH7701
#> "/home/biocbuild/.cache/R/ExperimentHub/1b25e03cfddb78_7751"
HiCExperiment
package can be used to import data provided by HiContactsData
.
Refer to HiCExperiment
package documentation for further information.
sessionInfo()
#> R version 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
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#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] HiContactsData_1.6.0 ExperimentHub_2.12.0 AnnotationHub_3.12.0
#> [4] BiocFileCache_2.12.0 dbplyr_2.5.0 BiocGenerics_0.50.0
#> [7] BiocStyle_2.32.0
#>
#> loaded via a namespace (and not attached):
#> [1] KEGGREST_1.44.0 xfun_0.43 bslib_0.7.0
#> [4] Biobase_2.64.0 vctrs_0.6.5 tools_4.4.0
#> [7] generics_0.1.3 stats4_4.4.0 curl_5.2.1
#> [10] tibble_3.2.1 fansi_1.0.6 AnnotationDbi_1.66.0
#> [13] RSQLite_2.3.6 blob_1.2.4 pkgconfig_2.0.3
#> [16] S4Vectors_0.42.0 lifecycle_1.0.4 GenomeInfoDbData_1.2.12
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