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## Loading required package: generics##
## Attaching package: 'generics'## The following objects are masked from 'package:base':
##
## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
## setequal, union##
## 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, is.unsorted, lapply,
## mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## rank, rbind, rownames, sapply, saveRDS, table, tapply, 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/25c9c4579dc56e_6166"DeepWt_T7.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6124
## "/home/biocbuild/.cache/R/ExperimentHub/25c9c46cf709d_6167"DeepWt_U6.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6125
## "/home/biocbuild/.cache/R/ExperimentHub/25c9c427cd4703_6168"esp_rnn_model.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6126
## "/home/biocbuild/.cache/R/ExperimentHub/25c9c43c838fb4_6169"hf_rnn_model.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6127
## "/home/biocbuild/.cache/R/ExperimentHub/25c9c461a9ae15_6170"# For Lindel model:
Model_weights.pkl()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6128
## "/home/biocbuild/.cache/R/ExperimentHub/25c9c45c41d935_6171"Or we can access them using the ExperimentHub interface:
eh <- ExperimentHub()
query(eh, "crisprScoreData")## ExperimentHub with 9 records
## # snapshotDate(): 2025-04-11
## # $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/25c9c461a9ae15_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 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## 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_1.12.0 ExperimentHub_2.16.0 AnnotationHub_3.16.0
## [4] BiocFileCache_2.16.0 dbplyr_2.5.0 BiocGenerics_0.54.0
## [7] generics_0.1.3 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.3 sass_0.4.10 BiocVersion_3.21.1
## [4] RSQLite_2.3.9 digest_0.6.37 magrittr_2.0.3
## [7] evaluate_1.0.3 bookdown_0.43 fastmap_1.2.0
## [10] blob_1.2.4 jsonlite_2.0.0 AnnotationDbi_1.70.0
## [13] GenomeInfoDb_1.44.0 DBI_1.2.3 BiocManager_1.30.25
## [16] httr_1.4.7 purrr_1.0.4 UCSC.utils_1.4.0
## [19] Biostrings_2.76.0 jquerylib_0.1.4 cli_3.6.4
## [22] crayon_1.5.3 rlang_1.1.6 XVector_0.48.0
## [25] Biobase_2.68.0 bit64_4.6.0-1 withr_3.0.2
## [28] cachem_1.1.0 yaml_2.3.10 tools_4.5.0
## [31] memoise_2.0.1 dplyr_1.1.4 GenomeInfoDbData_1.2.14
## [34] filelock_1.0.3 curl_6.2.2 mime_0.13
## [37] vctrs_0.6.5 R6_2.6.1 png_0.1-8
## [40] stats4_4.5.0 lifecycle_1.0.4 KEGGREST_1.48.0
## [43] S4Vectors_0.46.0 IRanges_2.42.0 bit_4.6.0
## [46] pkgconfig_2.0.3 pillar_1.10.2 bslib_0.9.0
## [49] glue_1.8.0 xfun_0.52 tibble_3.2.1
## [52] tidyselect_1.2.1 knitr_1.50 htmltools_0.5.8.1
## [55] rmarkdown_2.29 compiler_4.5.0