Analysis of high-throughput genetic perturbation screens in R.
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
R
data
docs
inst
man
pkgdown
tests
vignettes
.Rbuildignore
.gitattributes
.gitignore
.lintr
.travis.yml
DESCRIPTION
LICENSE
NAMESPACE
NEWS.md
README.md
appveyor.yml
code-of-conduct.md
codecov.yml
perturbatr.Rproj

README.md

perturbatr

Project Status Build Status Build app codecov bioc

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

Download the latest perturbatr release and install the package using:

  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