R/data.R

In perturbatr: Statistical Analysis of High-Throughput Genetic Perturbation Screens

# perturbatr: analysis of high-throughput gene perturbation screens
#
# Copyright (C) 2018 Simon Dirmeier
#
# This file is part of perturbatr
#
# perturbatr is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# perturbatr is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with perturbatr If not, see <http://www.gnu.org/licenses/>.


#' A sample pan-pathogenic perturbation dataset
#'
#' Example \code{PerturbationData} object of two integrated viral
#' RNAi screens that consists of a HCV kinome screen and a SARS kinome screen
#' (see references).
#'
#' @references
#'  de Wilde, Adriaan H., et al. (2015),
#'  A kinome-wide small interfering RNA screen identifies proviral and
#'  antiviral host factors in severe acute respiratory syndrome coronavirus
#'  replication, including double-stranded RNA-activated protein kinase and
#'  early secretory pathway proteins. \cr
#'  \emph{Journal of virology} \cr \cr
#'  Reiss, Simon, et al. (2011),
#'  Recruitment and activation of a lipid kinase by hepatitis C virus NS5A is
#'  essential for integrity of the membranous replication compartment.\cr
#'  \emph{Cell Host & Microbe} \cr \cr
#'
#'  Friedman J., Hastie T., Hoefling H. and Tibshirani R. (2007),
#'  Pathwise coordinate optimization.\cr
#'  \emph{The Annals of Applied Statistics}\cr \cr
#'
#' \itemize{
#'     \item Condition  \code{character} names of the viruses
#'     \item Replicate  \code{integer} replicate numer
#'     \item Plate  \code{integer} plate index
#'     \item RowIdx  \code{integer} row index of the well on the plate
#'     \item ColIdx  \code{integer} column index of the well on the plate
#'     \item GeneSymbol  \code{character} HugoSymbol of a gene
#'     \item Entrez  \code{integer} entrez ID
#'     \item ReadoutType  \code{character} readout type,
#'       such as `GFP` or `Luciferase`
#'     \item Control  \code{integer} coding of controls.
#'       `-1` for negative control, `1` for positive control, `0` for regular
#'        sample
#'     \item Library  \code{character} library type, such as `Ambion`
#'     \item Perturbation  \code{character} sirna identifier
#'     \item Screen  \code{character} identifier,
#'       for example `Kinome` or `Genome`
#'     \item Cell  \code{character} cell type, such as `Huh7.5`
#'     \item ScreenType \code{character} screen type, such as `E/R` for
#'        entry/replication
#'     \item Design  \code{character} design of the library, e.g. `pooled`
#'     \item Readout  \code{numeric} readout value, e.g. GFP measurement or read
#'        count
#'     \item ReadoutClass  \code{character} class of the readout, such as
#'       `Readout` or `Viability`
#'     \item NumCells  \code{integer} number of measured cells per well
#'  }
#
#' @author Simon Dirmeier | Computational Biology Group, ETH ZURICH |
#'  \email{simon.dirmeier@@web.de}
#'
#' @name rnaiscreen
#'
#' @docType data
#' @keywords datasets data
#' @usage data(rnaiscreen)
#' @format A \code{PerturbationData} object containing a \code{tibble} with 18
#'  columns, each describing a feature.
#'
#' @examples
#'  data(rnaiscreen)
#'  fit <- hm(rnaiscreen)
#'  pls <- plot(fit)
#'  pls[[1]]
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