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()## snapshotDate(): 2022-10-24## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6123
## "/home/biocbuild/.cache/R/ExperimentHub/b72b535bb7bab_6166"DeepWt_T7.hdf5()## snapshotDate(): 2022-10-24## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6124
## "/home/biocbuild/.cache/R/ExperimentHub/b72b519fdd9d8_6167"DeepWt_U6.hdf5()## snapshotDate(): 2022-10-24## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6125
## "/home/biocbuild/.cache/R/ExperimentHub/b72b53ba939d7_6168"esp_rnn_model.hdf5()## snapshotDate(): 2022-10-24## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6126
## "/home/biocbuild/.cache/R/ExperimentHub/b72b56cd9b8ba_6169"hf_rnn_model.hdf5()## snapshotDate(): 2022-10-24## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6127
## "/home/biocbuild/.cache/R/ExperimentHub/b72b5220f0848_6170"# For Lindel model:
Model_weights.pkl()## snapshotDate(): 2022-10-24## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6128
## "/home/biocbuild/.cache/R/ExperimentHub/b72b577a56bc9_6171"Or we can access them using the ExperimentHub interface:
eh <- ExperimentHub()## snapshotDate(): 2022-10-24query(eh, "crisprScoreData")## ExperimentHub with 9 records
## # snapshotDate(): 2022-10-24
## # $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/b72b5220f0848_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 Under development (unstable) (2022-10-25 r83175)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-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
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] crisprScoreData_1.3.0 ExperimentHub_2.7.0 AnnotationHub_3.7.0
## [4] BiocFileCache_2.7.0 dbplyr_2.2.1 BiocGenerics_0.45.0
## [7] BiocStyle_2.27.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.39.0 xfun_0.34
## [3] bslib_0.4.0 Biobase_2.59.0
## [5] bitops_1.0-7 vctrs_0.5.0
## [7] tools_4.3.0 generics_0.1.3
## [9] stats4_4.3.0 curl_4.3.3
## [11] tibble_3.1.8 fansi_1.0.3
## [13] AnnotationDbi_1.61.0 RSQLite_2.2.18
## [15] blob_1.2.3 pkgconfig_2.0.3
## [17] S4Vectors_0.37.0 assertthat_0.2.1
## [19] GenomeInfoDbData_1.2.9 lifecycle_1.0.3
## [21] compiler_4.3.0 stringr_1.4.1
## [23] Biostrings_2.67.0 GenomeInfoDb_1.35.0
## [25] httpuv_1.6.6 htmltools_0.5.3
## [27] sass_0.4.2 RCurl_1.98-1.9
## [29] yaml_2.3.6 interactiveDisplayBase_1.37.0
## [31] pillar_1.8.1 later_1.3.0
## [33] crayon_1.5.2 jquerylib_0.1.4
## [35] ellipsis_0.3.2 cachem_1.0.6
## [37] mime_0.12 tidyselect_1.2.0
## [39] digest_0.6.30 stringi_1.7.8
## [41] purrr_0.3.5 dplyr_1.0.10
## [43] bookdown_0.29 BiocVersion_3.17.0
## [45] fastmap_1.1.0 cli_3.4.1
## [47] magrittr_2.0.3 utf8_1.2.2
## [49] withr_2.5.0 filelock_1.0.2
## [51] promises_1.2.0.1 rappdirs_0.3.3
## [53] bit64_4.0.5 XVector_0.39.0
## [55] rmarkdown_2.17 httr_1.4.4
## [57] bit_4.0.4 png_0.1-7
## [59] memoise_2.0.1 shiny_1.7.3
## [61] evaluate_0.17 knitr_1.40
## [63] IRanges_2.33.0 rlang_1.0.6
## [65] Rcpp_1.0.9 xtable_1.8-4
## [67] glue_1.6.2 DBI_1.1.3
## [69] BiocManager_1.30.19 jsonlite_1.8.3
## [71] R6_2.5.1 zlibbioc_1.45.0