DESCRIPTION

Package: perturbatr
Type: Package
Title: Statistical Analysis of High-Throughput Genetic Perturbation Screens
Version: 1.7.0
Authors@R: person("Simon", "Dirmeier",
	email = "simon.dirmeier@web.de", role = c("aut", "cre"))
Maintainer: Simon Dirmeier <simon.dirmeier@web.de>
URL: https://github.com/cbg-ethz/perturbatr
BugReports: https://github.com/cbg-ethz/perturbatr/issues
Description: perturbatr does stage-wise analysis of large-scale genetic
 perturbation screens for integrated data sets consisting of multiple screens.
 For multiple integrated perturbation screens a hierarchical model that
 considers the variance between different biological conditions is fitted.
 The resulting list of gene effects is then further extended using a network
 propagation algorithm to correct for false negatives.
License: GPL-3
LazyData: TRUE
RoxygenNote: 6.1.1
Depends:
  R (>= 3.5),
  methods,
  stats
Imports:
    dplyr,
    ggplot2,
    tidyr,
    assertthat,
    lme4,
    splines,
    igraph,
    foreach,
    parallel,
    doParallel,
    diffusr,
    lazyeval,
    tibble,
    grid,
    utils,
    graphics,
    scales,
    magrittr,
    formula.tools,
    rlang
biocViews: ImmunoOncology, Regression, CellBasedAssays, Network
Suggests:
    testthat,
    lintr,
    knitr,
    rmarkdown,
    BiocStyle
VignetteBuilder: knitr
Collate:
    'methods_getters.R'
    'util_enums.R'
    'class_analysed.R'
    'class_data.R'
    'data.R'
    'inference_diffuse.R'
    'inference_diffuse_mrw.R'
    'inference_lmm_locfdr.R'
    'inference_lmm_fdr.R'
    'inference_lmm.R'
    'inference_lmm_model_data.R'
    'methods_combine.R'
    'methods_filter.R'
    'methods_show.R'
    'perturbatr-package.R'
    'plot_data.R'
    'plot_diffusion.R'
    'plot_hm.R'
    'util_as.R'
    'util_effect_matrix.R'
    'util_functions.R'
    'util_graph.R'
    'util_sampler.R'
Encoding: UTF-8