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## Loading required package: generics##
## Attaching package: 'generics'## The following objects are masked from 'package:base':
##
## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
## setequal, union##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:generics':
##
## intersect, setdiff, setequal, union## 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,
## setequal, 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()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6123
## "/home/biocbuild/.cache/R/ExperimentHub/1700ef6ebf3d1c_6166"DeepWt_T7.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6124
## "/home/biocbuild/.cache/R/ExperimentHub/1700ef9b2d5e5_6167"DeepWt_U6.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6125
## "/home/biocbuild/.cache/R/ExperimentHub/1700ef2c4aa20_6168"esp_rnn_model.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6126
## "/home/biocbuild/.cache/R/ExperimentHub/1700ef709f95cb_6169"hf_rnn_model.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6127
## "/home/biocbuild/.cache/R/ExperimentHub/1700ef62cd9a4c_6170"# For Lindel model:
Model_weights.pkl()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation
## loading from cache## EH6128
## "/home/biocbuild/.cache/R/ExperimentHub/1700ef7c02ad99_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.rdseh[["EH6127"]]## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6127
## "/home/biocbuild/.cache/R/ExperimentHub/1700ef62cd9a4c_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 Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-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_1.11.0 ExperimentHub_2.15.0 AnnotationHub_3.15.0
## [4] BiocFileCache_2.15.0 dbplyr_2.5.0 BiocGenerics_0.53.1
## [7] generics_0.1.3 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.47.0 xfun_0.49 bslib_0.8.0
## [4] Biobase_2.67.0 vctrs_0.6.5 tools_4.5.0
## [7] stats4_4.5.0 curl_5.2.3 tibble_3.2.1
## [10] fansi_1.0.6 AnnotationDbi_1.69.0 RSQLite_2.3.7
## [13] blob_1.2.4 pkgconfig_2.0.3 S4Vectors_0.45.0
## [16] lifecycle_1.0.4 GenomeInfoDbData_1.2.13 compiler_4.5.0
## [19] Biostrings_2.75.0 GenomeInfoDb_1.43.0 htmltools_0.5.8.1
## [22] sass_0.4.9 yaml_2.3.10 pillar_1.9.0
## [25] crayon_1.5.3 jquerylib_0.1.4 cachem_1.1.0
## [28] mime_0.12 tidyselect_1.2.1 digest_0.6.37
## [31] dplyr_1.1.4 purrr_1.0.2 bookdown_0.41
## [34] BiocVersion_3.21.1 fastmap_1.2.0 cli_3.6.3
## [37] magrittr_2.0.3 utf8_1.2.4 withr_3.0.2
## [40] filelock_1.0.3 UCSC.utils_1.3.0 rappdirs_0.3.3
## [43] bit64_4.5.2 rmarkdown_2.29 XVector_0.47.0
## [46] httr_1.4.7 bit_4.5.0 png_0.1-8
## [49] memoise_2.0.1 evaluate_1.0.1 knitr_1.48
## [52] IRanges_2.41.0 rlang_1.1.4 glue_1.8.0
## [55] DBI_1.2.3 BiocManager_1.30.25 jsonlite_1.8.9
## [58] R6_2.5.1 zlibbioc_1.53.0