Authors: Jean-Philippe Fortin
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':
##
## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min
## Loading required package: AnnotationHub
## Loading required package: BiocFileCache
## Loading required package: dbplyr
This 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(): 2022-08-23
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
## EH6123
## "/Users/fortinj2/Library/Caches/org.R-project.R/R/ExperimentHub/f2d463daf223_6166"
DeepWt_T7.hdf5()## snapshotDate(): 2022-08-23
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
## EH6124
## "/Users/fortinj2/Library/Caches/org.R-project.R/R/ExperimentHub/f2d43b4f0b0c_6167"
DeepWt_U6.hdf5()## snapshotDate(): 2022-08-23
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
## EH6125
## "/Users/fortinj2/Library/Caches/org.R-project.R/R/ExperimentHub/1646f4d5dfe8e_6168"
esp_rnn_model.hdf5()## snapshotDate(): 2022-08-23
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
## EH6126
## "/Users/fortinj2/Library/Caches/org.R-project.R/R/ExperimentHub/f2d4425e5f3f_6169"
hf_rnn_model.hdf5()## snapshotDate(): 2022-08-23
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
## EH6127
## "/Users/fortinj2/Library/Caches/org.R-project.R/R/ExperimentHub/f2d441bf4323_6170"
# For Lindel model:
Model_weights.pkl()## snapshotDate(): 2022-08-23
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
## EH6128
## "/Users/fortinj2/Library/Caches/org.R-project.R/R/ExperimentHub/f2d473d0d08d_6171"
Or we can access them using the ExperimentHub interface:
eh <- ExperimentHub()## snapshotDate(): 2022-08-23
query(eh, "crisprScoreData")## ExperimentHub with 9 records
## # snapshotDate(): 2022-08-23
## # $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.rds
eh[["EH6127"]]## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
## EH6127
## "/Users/fortinj2/Library/Caches/org.R-project.R/R/ExperimentHub/f2d441bf4323_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.Rmd
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] crisprScoreData_1.1.3 ExperimentHub_2.5.0 AnnotationHub_3.5.0
## [4] BiocFileCache_2.5.0 dbplyr_2.2.1 BiocGenerics_0.43.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.9 png_0.1-7
## [3] Biostrings_2.65.2 assertthat_0.2.1
## [5] digest_0.6.29 utf8_1.2.2
## [7] mime_0.12 R6_2.5.1
## [9] GenomeInfoDb_1.33.5 stats4_4.2.1
## [11] RSQLite_2.2.16 evaluate_0.16
## [13] httr_1.4.4 pillar_1.8.1
## [15] zlibbioc_1.43.0 rlang_1.0.4
## [17] curl_4.3.2 rstudioapi_0.14
## [19] blob_1.2.3 S4Vectors_0.35.1
## [21] rmarkdown_2.15.2 stringr_1.4.1
## [23] RCurl_1.98-1.8 bit_4.0.4
## [25] shiny_1.7.2 compiler_4.2.1
## [27] httpuv_1.6.5 xfun_0.32
## [29] pkgconfig_2.0.3 htmltools_0.5.3
## [31] tidyselect_1.1.2 KEGGREST_1.37.3
## [33] tibble_3.1.8 GenomeInfoDbData_1.2.8
## [35] interactiveDisplayBase_1.35.0 IRanges_2.31.2
## [37] fansi_1.0.3 crayon_1.5.1
## [39] dplyr_1.0.9 later_1.3.0
## [41] bitops_1.0-7 rappdirs_0.3.3
## [43] xtable_1.8-4 lifecycle_1.0.1
## [45] DBI_1.1.3 magrittr_2.0.3
## [47] cli_3.3.0 stringi_1.7.8
## [49] cachem_1.0.6 XVector_0.37.0
## [51] promises_1.2.0.1 ellipsis_0.3.2
## [53] filelock_1.0.2 generics_0.1.3
## [55] vctrs_0.4.1 tools_4.2.1
## [57] bit64_4.0.5 Biobase_2.57.1
## [59] glue_1.6.2 purrr_0.3.4
## [61] BiocVersion_3.16.0 fastmap_1.1.0
## [63] yaml_2.3.5 AnnotationDbi_1.59.1
## [65] BiocManager_1.30.18 memoise_2.0.1
## [67] knitr_1.40
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