Package: sceptre
Title: Analysis of Single-Cell CRISPR Screen Data
Version: 0.99.0
Authors@R: 
    c(person(given = "Timothy",
           family = "Barry",
           role = c("aut", "cre"),
           email = "tbarry@hsph.harvard.edu",
           comment=c(ORCID="0000-0002-4356-627X")),
           person(given = "Louis",
           family = "Deutsch",
           role = c("aut")),
           person(given = "Eugene",
           family = "Katsevich",
           role = c("aut"),
           email = "ekatsevi@wharton.upenn.edu",
           comment = c(ORCID = "0000-0003-0598-2050")),
           person(given = "Wharton Analytics",
           role = "fnd"),
           person(given = "National Science Foundation",
           role = "fnd",
           comment = "Grants DMS-2113072 and DMS-2310654")
           )
Description: Analysis of single-cell CRISPR screen data. `sceptre` is
        an R package for statistically rigorous, massively scalable,
        and user-friendly single-cell CRISPR screen data analysis. The
        `sceptre` pipeline consists of several distinct steps: (1)
        import data from 10X CellRanger, Parse CRISPR Detect, or a set
        of R objects; (2) set the analysis parameters, which are the
        parameters that govern how the statistical analysis is to be
        conducted; (3) assign gRNAs to cells using one of three
        methods; (4) run quality control; (5) run the calibration
        check, which is an analysis that verifies that `sceptre`
        controls the rate of false positives on the dataset under
        analysis; (6) run the power check, which is an analysis that
        verifies that `sceptre` is capable of discovering true
        associations on the dataset under analysis; (7) run the
        discovery analysis to identify high-confidence
        perturbation-gene links among the set of perturbations and
        genes whose association status we do not know but seek to
        learn; (8) write outputs to a specified directory, including
        results, plots, and a textual summary of the analysis.
        `sceptre` leverages several novel statistical and computational
        algorithms to achieve high statistical accuracy and
        computational speed.
License: GPL-3
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
LinkingTo: Rcpp, BH
biocViews: CRISPR, SingleCell, DifferentialExpression, GeneRegulation,
        DataImport, GeneTarget
Imports: cowplot, crayon, data.table, dplyr, ggplot2, Matrix, methods,
        Rcpp (>= 1.0.9), scales, stats, purrr, parallelly (>= 1.23.0),
        withr
Depends: R (>= 4.1)
Suggests: ondisc, R.utils, sessioninfo, rmarkdown, knitr, testthat (>=
        3.0.0), MASS
VignetteBuilder: knitr
Config/testthat/edition: 3
URL: https://katsevich-lab.github.io/sceptre/,
        https://timothy-barry.github.io/sceptre-book/,
        https://github.com/Katsevich-Lab/sceptre
BugReports: https://github.com/Katsevich-Lab/sceptre/issues
LazyData: false
Repository: https://biocstaging.r-universe.dev
Date/Publication: 2026-08-31 16:31:54 UTC
RemoteUrl: https://github.com/BiocStaging/sceptre
RemoteRef: HEAD
RemoteSha: 21f9ea098b69c444a884a49bc254de711968e3b5
NeedsCompilation: yes
Packaged: 2026-09-30 12:47:20 UTC; root
Author: Timothy Barry [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-4356-627X>),
  Louis Deutsch [aut],
  Eugene Katsevich [aut] (ORCID: <https://orcid.org/0000-0003-0598-2050>),
  Wharton Analytics [fnd],
  National Science Foundation [fnd] (Grants DMS-2113072 and DMS-2310654)
Maintainer: Timothy Barry <tbarry@hsph.harvard.edu>