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\"crisprScoreData\"\noutput: \n  github_document:\n    toc: true\n---\n\n```{r, echo=FALSE, results=\"hide\"}\noptions(\"knitr.graphics.auto_pdf\"=TRUE)\n```\n\nAuthors: Jean-Philippe Fortin\n\n\n# Installation from Bioconductor\n\n`crisprScoreData` can be installed from the Bioconductor devel\nbranch using the following commands in a fresh R session:\n\n```{r, eval=FALSE}\nif (!requireNamespace(\"BiocManager\", quietly = TRUE))\n    install.packages(\"BiocManager\")\n\nBiocManager::install(version=\"devel\")\nBiocManager::install(\"crisprScoreData\")\n```\n\n\n# Exploring the different data in crisprScoreData\n\nWe first load the `crisprScoreData` package:\n\n```{r}\nlibrary(crisprScoreData)\n```\n\nThis package contains several pre-trained models for different\non-target activity prediction algorithms to be used \nin the package *crisprScore*.\n\nWe can access the file paths of the different \npre-trained models directly with named functions:\n\n```{r}\n# For DeepHF model:\nDeepWt.hdf5()\nDeepWt_T7.hdf5()\nDeepWt_U6.hdf5()\nesp_rnn_model.hdf5()\nhf_rnn_model.hdf5()\n\n# For Lindel model:\nModel_weights.pkl()\n```\n\n\nOr we can access them using the *ExperimentHub* interface:\n\n```{r}\neh \u003c- ExperimentHub()\nquery(eh, \"crisprScoreData\")\neh[[\"EH6127\"]]\n```\n\nFor details on the source of these files, and on their construction\nsee `?crisprScoreData` and the scripts:\n\n* `inst/scripts/make-metadata.R`\n* `inst/scripts/make-data.Rmd`\n\n```{r}\nsessionInfo()\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcrisprverse%2Fcrisprscoredata","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcrisprverse%2Fcrisprscoredata","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcrisprverse%2Fcrisprscoredata/lists"}