README.md

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

perturbatr

Analysis of high-throughput gene perturbation screens in R.

Introduction

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. That means that we first estimate relative effect sizes for all genes. The resulting hit lists is then further extended using a network propagation algorithm to correct for false negatives. and positives.

data(rnaiscreen)
graph <- readRDS(
  system.file("extdata", "graph_file.tsv", package = "perturbatr"))

frm   <- Readout ~ Condition +
                   (1|GeneSymbol) + (1|Condition:GeneSymbol) +
                   (1|ScreenType) + (1|Condition:ScreenType)
ft    <- hm(rnaiscreen, formula = frm)
diffu <- diffuse(ft, graph=graph, r=0.3)

plot(diffu)

Installation

You can install and use perturbatr either as an R library from Bioconductor, or by downloading the tarball.

If you want to use the recommended way using Bioconductor just call:

if (!requireNamespace("BiocManager", quietly=TRUE))
  install.packages("BiocManager")
BiocManager::install("perturbatr")

library(perturbatr)

from the R-console.

Installing the package using the downloaded tarball works like this:

  R CMD install <perturbatr.tar.gz>

where perturbatr.tar.gz is the downloaded tarball.

Documentation

Load the package using library(perturbatr). We provide a vignette for the package that can be called using: vignette("perturbatr").

Author

• Simon Dirmeier [e-mail]

cbg-ethz/perturbatr documentation built on Feb. 11, 2020, 4:21 p.m.