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, 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()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                       EH6123 
## "/home/biocbuild/.cache/R/ExperimentHub/1c023115cd7ed9_6166"
DeepWt_T7.hdf5()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                       EH6124 
## "/home/biocbuild/.cache/R/ExperimentHub/1c02316ac5a71c_6167"
DeepWt_U6.hdf5()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                       EH6125 
## "/home/biocbuild/.cache/R/ExperimentHub/1c02312c1e4082_6168"
esp_rnn_model.hdf5()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                       EH6126 
## "/home/biocbuild/.cache/R/ExperimentHub/1c023163501ba3_6169"
hf_rnn_model.hdf5()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                       EH6127 
## "/home/biocbuild/.cache/R/ExperimentHub/1c023141a1a230_6170"
# For Lindel model:
Model_weights.pkl()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                       EH6128 
## "/home/biocbuild/.cache/R/ExperimentHub/1c023115b52952_6171"

Or we can access them using the ExperimentHub interface:

eh <- ExperimentHub()
query(eh, "crisprScoreData")
## ExperimentHub with 9 records
## # snapshotDate(): 2023-10-24
## # $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/1c023141a1a230_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 Under development (unstable) (2023-10-22 r85388)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-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       
## 
## 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_1.7.0 ExperimentHub_2.11.0  AnnotationHub_3.11.0 
## [4] BiocFileCache_2.11.1  dbplyr_2.4.0          BiocGenerics_0.49.0  
## [7] BiocStyle_2.31.0     
## 
## loaded via a namespace (and not attached):
##  [1] KEGGREST_1.43.0               xfun_0.40                    
##  [3] bslib_0.5.1                   Biobase_2.63.0               
##  [5] bitops_1.0-7                  vctrs_0.6.4                  
##  [7] tools_4.4.0                   generics_0.1.3               
##  [9] stats4_4.4.0                  curl_5.1.0                   
## [11] tibble_3.2.1                  fansi_1.0.5                  
## [13] AnnotationDbi_1.65.0          RSQLite_2.3.2                
## [15] blob_1.2.4                    pkgconfig_2.0.3              
## [17] S4Vectors_0.41.1              GenomeInfoDbData_1.2.11      
## [19] lifecycle_1.0.3               compiler_4.4.0               
## [21] Biostrings_2.71.1             GenomeInfoDb_1.39.0          
## [23] httpuv_1.6.12                 htmltools_0.5.6.1            
## [25] sass_0.4.7                    RCurl_1.98-1.12              
## [27] yaml_2.3.7                    interactiveDisplayBase_1.41.0
## [29] pillar_1.9.0                  later_1.3.1                  
## [31] crayon_1.5.2                  jquerylib_0.1.4              
## [33] ellipsis_0.3.2                cachem_1.0.8                 
## [35] mime_0.12                     tidyselect_1.2.0             
## [37] digest_0.6.33                 purrr_1.0.2                  
## [39] dplyr_1.1.3                   bookdown_0.36                
## [41] BiocVersion_3.19.1            fastmap_1.1.1                
## [43] cli_3.6.1                     magrittr_2.0.3               
## [45] utf8_1.2.4                    withr_2.5.2                  
## [47] filelock_1.0.2                promises_1.2.1               
## [49] rappdirs_0.3.3                bit64_4.0.5                  
## [51] rmarkdown_2.25                XVector_0.43.0               
## [53] httr_1.4.7                    bit_4.0.5                    
## [55] png_0.1-8                     memoise_2.0.1                
## [57] shiny_1.7.5.1                 evaluate_0.22                
## [59] knitr_1.45                    IRanges_2.37.0               
## [61] rlang_1.1.1                   Rcpp_1.0.11                  
## [63] xtable_1.8-4                  glue_1.6.2                   
## [65] DBI_1.1.3                     BiocManager_1.30.22          
## [67] jsonlite_1.8.7                R6_2.5.1                     
## [69] zlibbioc_1.49.0