Package: perturbatr
Type: Package
Title: Statistical Analysis of High-Throughput Genetic Perturbation
        Screens
Version: 1.0.0
Date: 2018-03-19
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: 6.0.1
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: 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'
NeedsCompilation: no
Packaged: 2018-05-01 04:32:06 UTC; biocbuild
Author: Simon Dirmeier [aut, cre]
Built: R 3.5.0; ; 2018-05-01 12:31:13 UTC; windows
