crisprScoreData can be installed from the Bioconductor devel
branch using the following commands in a fresh R session:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version="devel")
BiocManager::install("crisprScoreData")We first load the crisprScoreData package:
library(crisprScoreData)## Loading required package: ExperimentHub## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which.max, which.min## Loading required package: AnnotationHub## Loading required package: BiocFileCache## Loading required package: dbplyrThis package contains several pre-trained models for different on-target activity prediction algorithms to be used in the package crisprScore.
We can access the file paths of the different pre-trained models directly with named functions:
# For DeepHF model:
DeepWt.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6123
## "/home/biocbuild/.cache/R/ExperimentHub/6cf8c4d5db5a1_6166"DeepWt_T7.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6124
## "/home/biocbuild/.cache/R/ExperimentHub/6cf8c248c766e_6167"DeepWt_U6.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6125
## "/home/biocbuild/.cache/R/ExperimentHub/6cf8c7eb5c290_6168"esp_rnn_model.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6126
## "/home/biocbuild/.cache/R/ExperimentHub/6cf8c58680593_6169"hf_rnn_model.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6127
## "/home/biocbuild/.cache/R/ExperimentHub/6cf8cc21d30e_6170"# For Lindel model:
Model_weights.pkl()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6128
## "/home/biocbuild/.cache/R/ExperimentHub/6cf8c2275f50b_6171"Or we can access them using the ExperimentHub interface:
eh <- ExperimentHub()
query(eh, "crisprScoreData")## ExperimentHub with 9 records
## # snapshotDate(): 2023-10-24
## # $dataprovider: Fudan University, UCSF, University of Washington, New York ...
## # $species: NA
## # $rdataclass: character
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH6123"]]'
##
## title
## EH6123 | DeepWt.hdf5
## EH6124 | DeepWt_T7.hdf5
## EH6125 | DeepWt_U6.hdf5
## EH6126 | esp_rnn_model.hdf5
## EH6127 | hf_rnn_model.hdf5
## EH6128 | Model_weights.pkl
## EH7304 | CRISPRa_model.pkl
## EH7305 | CRISPRi_model.pkl
## EH7356 | RFcombined.rdseh[["EH6127"]]## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6127
## "/home/biocbuild/.cache/R/ExperimentHub/6cf8cc21d30e_6170"For details on the source of these files, and on their construction
see ?crisprScoreData and the scripts:
inst/scripts/make-metadata.Rinst/scripts/make-data.RmdsessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-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
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [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] crisprScoreData_1.6.0 ExperimentHub_2.10.0 AnnotationHub_3.10.0
## [4] BiocFileCache_2.10.0 dbplyr_2.3.4 BiocGenerics_0.48.0
## [7] BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.42.0 xfun_0.40
## [3] bslib_0.5.1 Biobase_2.62.0
## [5] bitops_1.0-7 vctrs_0.6.4
## [7] tools_4.3.1 generics_0.1.3
## [9] stats4_4.3.1 curl_5.1.0
## [11] tibble_3.2.1 fansi_1.0.5
## [13] AnnotationDbi_1.64.0 RSQLite_2.3.1
## [15] blob_1.2.4 pkgconfig_2.0.3
## [17] S4Vectors_0.40.0 GenomeInfoDbData_1.2.11
## [19] lifecycle_1.0.3 compiler_4.3.1
## [21] Biostrings_2.70.1 GenomeInfoDb_1.38.0
## [23] httpuv_1.6.12 htmltools_0.5.6.1
## [25] sass_0.4.7 RCurl_1.98-1.12
## [27] yaml_2.3.7 interactiveDisplayBase_1.40.0
## [29] pillar_1.9.0 later_1.3.1
## [31] crayon_1.5.2 jquerylib_0.1.4
## [33] ellipsis_0.3.2 cachem_1.0.8
## [35] mime_0.12 tidyselect_1.2.0
## [37] digest_0.6.33 purrr_1.0.2
## [39] dplyr_1.1.3 bookdown_0.36
## [41] BiocVersion_3.18.0 fastmap_1.1.1
## [43] cli_3.6.1 magrittr_2.0.3
## [45] utf8_1.2.4 withr_2.5.1
## [47] filelock_1.0.2 promises_1.2.1
## [49] rappdirs_0.3.3 bit64_4.0.5
## [51] rmarkdown_2.25 XVector_0.42.0
## [53] httr_1.4.7 bit_4.0.5
## [55] png_0.1-8 memoise_2.0.1
## [57] shiny_1.7.5.1 evaluate_0.22
## [59] knitr_1.44 IRanges_2.36.0
## [61] rlang_1.1.1 Rcpp_1.0.11
## [63] xtable_1.8-4 glue_1.6.2
## [65] DBI_1.1.3 BiocManager_1.30.22
## [67] jsonlite_1.8.7 R6_2.5.1
## [69] zlibbioc_1.48.0