https://github.com/lucidif/HiCeekR
R package that allows easily performing a complete Hi-C data analysis through a Graphical User Interface
https://github.com/lucidif/HiCeekR
Last synced: 4 months ago
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R package that allows easily performing a complete Hi-C data analysis through a Graphical User Interface
- Host: GitHub
- URL: https://github.com/lucidif/HiCeekR
- Owner: lucidif
- License: gpl-2.0
- Created: 2018-09-13T15:51:25.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-01-26T07:40:25.000Z (about 4 years ago)
- Last Synced: 2024-08-13T07:14:44.200Z (8 months ago)
- Language: R
- Size: 3.55 MB
- Stars: 15
- Watchers: 0
- Forks: 8
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# HiCeekR
HiCeekR is a novel Shiny based R package for Hi-C data analysis.
HiCeekR combines several R/Bioconductor packages widely used for Hi-C data analysis and visualization. It
starts from already aligned sequence files obtained from Hi-C experiments, then proceeds through a
series of steps from pre-processing and filtering, to the evaluation and normalization of the contact
matrices. Once the contact matrices are available, HiCeekR allows the users to perform several
downstream analyses. Moreover, HiCeekR produces several interactive graphics that allow exploring the
results by the usage of the mouse pointer.
Thanks to its GUI, HiCeekR friendly guides users during the entire analysis process, allowing them to
perform a complete data analysis pipeline (i.e., pre-processing, filtering, binning, normalization,
identification of compartments and TADs) and to integrate Hi-C data with other omic datasets such as
ChIP-seq and RNA-seq.
# References
If you're using HiCeekR for your analysis please cite:
Di Filippo, L., Righelli, D., Gagliardi, M., Matarazzo, M. R., & Angelini, C. (2019). HiCeekR: a novel Shiny app for Hi-C data analysis. Frontiers in Genetics, 10, 1079. https://doi.org/10.3389/fgene.2019.01079
## Installation
The easiest way to install HiCeekR is via GitHub performing the following steps:
i) Download and install the devtools R package from CRAN web site.
Open the R console and digit:
````
install.packages("devtools")
````
ii) Download and install BiocManager from Bioconductor website.
In the R console digit:
````
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
````
iii) Now install HiCeekR using the command:
````
library(devtools) ; install_github("HenrikBengtsson/TopDom") ; install_github("lucidif/HiCeekR", repos=BiocManager::repositories())
````
## Get started
Once installed, launch HiCeekR Shiny app with the following commands:
````
library(HiCeekR)
HiCeekR()
````
For more details, please consult the documentation (https://github.com/lucidif/HiCeekR/blob/master/hiceekr_manual.pdf)
## Example Data
In order to understand and test HiCeekR functionalities, we provided some example data, available at http://bioinfo.na.iac.cnr.it/hiceekr
Two folders are available:
a) Annotations.zip contains the reference genome in FASTA format and the HiCeekR annotations tsv files.
b) Example.zip contains an example of all input files supported by HiCeekR:
1) sortmark_REP7_SRR1802426.bam and sortmark_REP5_SRR1802424.bam Hi-C aligned reads files (Grubert 2015, GSE62742)
2) ENCFF000ATY_H3K9ac_GM12878_hg19.bam/.bai and H3K27me3.bam/.bai ChiP-seq Bam files from ENCODE (ENCSR447YYN) and their index .bai files.
3) GSM2400247_ENCFF383EXA_gm12878_rnaSeq.tsv expression RNA-Seq Data from ENCODE (ENCFF383EXA)
## Session info
````
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
locale:
[1] LC_CTYPE=it_IT.UTF-8 LC_NUMERIC=C LC_TIME=it_IT.UTF-8
[4] LC_COLLATE=it_IT.UTF-8 LC_MONETARY=it_IT.UTF-8 LC_MESSAGES=it_IT.UTF-8
[7] LC_PAPER=it_IT.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] shiny_1.3.2 HiCeekR_0.99.0
loaded via a namespace (and not attached):
[1] R.utils_2.9.0 HiTC_1.28.0 tidyselect_0.2.5
[4] heatmaply_0.16.0 RSQLite_2.1.2 AnnotationDbi_1.46.1
[7] htmlwidgets_1.3 grid_3.6.1 TSP_1.1-7
[10] BiocParallel_1.18.1 devtools_2.2.0 munsell_0.5.0
[13] codetools_0.2-16 DT_0.9 withr_2.1.2
[16] colorspace_1.4-1 OrganismDbi_1.26.0 Biobase_2.44.0
[19] Category_2.50.0 knitr_1.25 rstudioapi_0.10
[22] stats4_3.6.1 GenomeInfoDbData_1.2.1 hwriter_1.3.2
[25] bit64_0.9-7 rhdf5_2.28.0 rprojroot_1.3-2
[28] vctrs_0.2.0 xfun_0.9 biovizBase_1.32.0
[31] csaw_1.18.0 gclus_1.3.2 R6_2.4.0
[34] GenomeInfoDb_1.20.0 seriation_1.2-8 locfit_1.5-9.1
[37] AnnotationFilter_1.8.0 reshape_0.8.8 diffHic_1.16.0
[40] bitops_1.0-6 DelayedArray_0.10.0 assertthat_0.2.1
[43] promises_1.0.1 networkD3_0.4 scales_1.0.0
[46] nnet_7.3-12 gtable_0.3.0 processx_3.4.1
[49] ggbio_1.32.0 ensembldb_2.8.0 rlang_0.4.0
[52] zeallot_0.1.0 genefilter_1.66.0 splines_3.6.1
[55] rtracklayer_1.44.4 lazyeval_0.2.2 acepack_1.4.1
[58] dichromat_2.0-0 checkmate_1.9.4 BiocManager_1.30.4
[61] reshape2_1.4.3 yaml_2.2.0 GenomicFeatures_1.36.4
[64] backports_1.1.4 httpuv_1.5.2 Hmisc_4.2-0
[67] RBGL_1.60.0 tools_3.6.1 usethis_1.5.1
[70] ggplot2_3.2.1 ellipsis_0.2.0.1 gplots_3.0.1.1
[73] RColorBrewer_1.1-2 HiCseg_1.1 BiocGenerics_0.30.0
[76] wavethresh_4.6.8 sessioninfo_1.1.1 plyr_1.8.4
[79] Rcpp_1.0.2 base64enc_0.1-3 progress_1.2.2
[82] zlibbioc_1.30.0 purrr_0.3.2 RCurl_1.95-4.12
[85] ps_1.3.0 prettyunits_1.0.2 rpart_4.1-15
[88] viridis_0.5.1 S4Vectors_0.22.1 SummarizedExperiment_1.14.1
[91] cluster_2.1.0 chromoR_1.0 fs_1.3.1
[94] magrittr_1.5 data.table_1.12.2 ProtGenerics_1.16.0
[97] matrixStats_0.55.0 haarfisz_4.5 pkgload_1.0.2
[100] hms_0.5.1 shinyjs_1.0 mime_0.7
[103] xtable_1.8-4 XML_3.98-1.20 IRanges_2.18.2
[106] gridExtra_2.3 testthat_2.2.1 compiler_3.6.1
[109] biomaRt_2.40.4 tibble_2.1.3 KernSmooth_2.23-15
[112] crayon_1.3.4 R.oo_1.22.0 ReportingTools_2.24.0
[115] htmltools_0.3.6 GOstats_2.50.0 later_0.8.0
[118] Formula_1.2-3 tidyr_1.0.0 geneplotter_1.62.0
[121] DBI_1.0.0 corrplot_0.84 MASS_7.3-51.1
[124] Matrix_1.2-17 cli_1.1.0 R.methodsS3_1.7.1
[127] gdata_2.18.0 parallel_3.6.1 igraph_1.2.4.1
[130] GenomicRanges_1.36.1 pkgconfig_2.0.2 GenomicAlignments_1.20.1
[133] registry_0.5-1 foreign_0.8-72 plotly_4.9.0
[136] InteractionSet_1.12.0 foreach_1.4.7 annotate_1.62.0
[139] webshot_0.5.1 XVector_0.24.0 AnnotationForge_1.26.0
[142] VariantAnnotation_1.30.1 stringr_1.4.0 callr_3.3.1
[145] digest_0.6.20 graph_1.62.0 Biostrings_2.52.0
[148] htmlTable_1.13.1 gProfileR_0.6.8 dendextend_1.12.0
[151] edgeR_3.26.8 GSEABase_1.46.0 curl_4.1
[154] Rsamtools_2.0.0 gtools_3.8.1 lifecycle_0.1.0
[157] jsonlite_1.6 PFAM.db_3.8.2 Rhdf5lib_1.6.1
[160] desc_1.2.0 viridisLite_0.3.0 limma_3.40.6
[163] BSgenome_1.52.0 pillar_1.4.2 GGally_1.4.0
[166] lattice_0.20-38 shinyFiles_0.7.3 Rhtslib_1.16.1
[169] httr_1.4.1 pkgbuild_1.0.5 survival_2.43-3
[172] GO.db_3.8.2 glue_1.3.1 remotes_2.1.0
[175] iterators_1.0.12 bit_1.1-14 Rgraphviz_2.28.0
[178] stringi_1.4.3 blob_1.2.0 DESeq2_1.24.0
[181] latticeExtra_0.6-28 caTools_1.17.1.2 memoise_1.1.0
[184] dplyr_0.8.3 TopDom_0.8.1
````