1 Installation from Bioconductor

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")

2 Exploring the different data in 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: 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(): 2022-10-24
## 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(): 2022-10-24
query(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.rds
eh[["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.R
  • inst/scripts/make-data.Rmd
sessionInfo()
## 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