• 1 Installation from Bioconductor
  • 2 Exploring the different data in crisprScoreData

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, saveRDS, 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/4daa04a0b1e15_6166"
DeepWt_T7.hdf5()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                      EH6124 
## "/home/biocbuild/.cache/R/ExperimentHub/4daa049a3dc50_6167"
DeepWt_U6.hdf5()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                      EH6125 
## "/home/biocbuild/.cache/R/ExperimentHub/4daa05c2b3949_6168"
esp_rnn_model.hdf5()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                      EH6126 
## "/home/biocbuild/.cache/R/ExperimentHub/4daa03a3e2c7e_6169"
hf_rnn_model.hdf5()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                      EH6127 
## "/home/biocbuild/.cache/R/ExperimentHub/4daa02fd33d7a_6170"
# For Lindel model:
Model_weights.pkl()
## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache
##                                                    EH6128 
## "/home/biocbuild/.cache/R/ExperimentHub/4daa0d929d9_6171"

Or we can access them using the ExperimentHub interface:

eh <- ExperimentHub()
query(eh, "crisprScoreData")
## ExperimentHub with 9 records
## # snapshotDate(): 2024-10-24
## # $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/4daa02fd33d7a_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 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
## 
## 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_1.10.0 ExperimentHub_2.14.0   AnnotationHub_3.14.0  
## [4] BiocFileCache_2.14.0   dbplyr_2.5.0           BiocGenerics_0.52.0   
## [7] BiocStyle_2.34.0      
## 
## loaded via a namespace (and not attached):
##  [1] KEGGREST_1.46.0         xfun_0.48               bslib_0.8.0            
##  [4] Biobase_2.66.0          vctrs_0.6.5             tools_4.4.1            
##  [7] generics_0.1.3          stats4_4.4.1            curl_5.2.3             
## [10] tibble_3.2.1            fansi_1.0.6             AnnotationDbi_1.68.0   
## [13] RSQLite_2.3.7           blob_1.2.4              pkgconfig_2.0.3        
## [16] S4Vectors_0.44.0        lifecycle_1.0.4         GenomeInfoDbData_1.2.13
## [19] compiler_4.4.1          Biostrings_2.74.0       GenomeInfoDb_1.42.0    
## [22] htmltools_0.5.8.1       sass_0.4.9              yaml_2.3.10            
## [25] pillar_1.9.0            crayon_1.5.3            jquerylib_0.1.4        
## [28] cachem_1.1.0            mime_0.12               tidyselect_1.2.1       
## [31] digest_0.6.37           dplyr_1.1.4             purrr_1.0.2            
## [34] bookdown_0.41           BiocVersion_3.20.0      fastmap_1.2.0          
## [37] cli_3.6.3               magrittr_2.0.3          utf8_1.2.4             
## [40] withr_3.0.2             filelock_1.0.3          UCSC.utils_1.2.0       
## [43] rappdirs_0.3.3          bit64_4.5.2             rmarkdown_2.28         
## [46] XVector_0.46.0          httr_1.4.7              bit_4.5.0              
## [49] png_0.1-8               memoise_2.0.1           evaluate_1.0.1         
## [52] knitr_1.48              IRanges_2.40.0          rlang_1.1.4            
## [55] glue_1.8.0              DBI_1.2.3               BiocManager_1.30.25    
## [58] jsonlite_1.8.9          R6_2.5.1                zlibbioc_1.52.0