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/24583743b62920_6166"DeepWt_T7.hdf5()## snapshotDate(): 2022-10-24## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6124
## "/home/biocbuild/.cache/R/ExperimentHub/2458372fc4a6ec_6167"DeepWt_U6.hdf5()## snapshotDate(): 2022-10-24## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6125
## "/home/biocbuild/.cache/R/ExperimentHub/24583769d8394a_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/24583771a10370_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/2458376dc5f4e8_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/24583752cc3480_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/2458376dc5f4e8_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 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
##
## 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_1.2.0 ExperimentHub_2.6.0 AnnotationHub_3.6.0
## [4] BiocFileCache_2.6.0 dbplyr_2.2.1 BiocGenerics_0.44.0
## [7] BiocStyle_2.26.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.9 png_0.1-7
## [3] Biostrings_2.66.0 assertthat_0.2.1
## [5] digest_0.6.30 utf8_1.2.2
## [7] mime_0.12 R6_2.5.1
## [9] GenomeInfoDb_1.34.0 stats4_4.2.1
## [11] RSQLite_2.2.18 evaluate_0.17
## [13] httr_1.4.4 pillar_1.8.1
## [15] zlibbioc_1.44.0 rlang_1.0.6
## [17] curl_4.3.3 jquerylib_0.1.4
## [19] blob_1.2.3 S4Vectors_0.36.0
## [21] rmarkdown_2.17 stringr_1.4.1
## [23] RCurl_1.98-1.9 bit_4.0.4
## [25] shiny_1.7.3 compiler_4.2.1
## [27] httpuv_1.6.6 xfun_0.34
## [29] pkgconfig_2.0.3 htmltools_0.5.3
## [31] tidyselect_1.2.0 KEGGREST_1.38.0
## [33] GenomeInfoDbData_1.2.9 tibble_3.1.8
## [35] interactiveDisplayBase_1.36.0 bookdown_0.29
## [37] IRanges_2.32.0 fansi_1.0.3
## [39] withr_2.5.0 crayon_1.5.2
## [41] dplyr_1.0.10 later_1.3.0
## [43] bitops_1.0-7 rappdirs_0.3.3
## [45] jsonlite_1.8.3 xtable_1.8-4
## [47] lifecycle_1.0.3 DBI_1.1.3
## [49] magrittr_2.0.3 cli_3.4.1
## [51] stringi_1.7.8 cachem_1.0.6
## [53] XVector_0.38.0 promises_1.2.0.1
## [55] bslib_0.4.0 ellipsis_0.3.2
## [57] filelock_1.0.2 generics_0.1.3
## [59] vctrs_0.5.0 tools_4.2.1
## [61] bit64_4.0.5 Biobase_2.58.0
## [63] glue_1.6.2 purrr_0.3.5
## [65] BiocVersion_3.16.0 fastmap_1.1.0
## [67] yaml_2.3.6 AnnotationDbi_1.60.0
## [69] BiocManager_1.30.19 memoise_2.0.1
## [71] knitr_1.40 sass_0.4.2