neellab/simem: Prediction of differential gene essentiality from pooled phenotypic screens (eg: siRNA, shRNA, CRISPR/Cas9 screens) using linear mixed effect models.

The siMEM (si/shRNA Interference Mixed Effects Model) algorithm predicts differential essentiality from pooled phenotypic screens. Such screens typically have multiple reagents (e.g., shRNAs, gRNAs) targeting each gene, and are performed across multiple, genetically heterogeneous, cell lines/samples. The algorithm is detailed in Marcotte et al. 2016 ("The Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities and Resistances."). Pooled screen data formatted for use with the siMEM algorithm can be found at http://neellab.github.io/bfg.

Getting started

Package details

Maintainer
LicenseMIT
Version1
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("neellab/simem")
neellab/simem documentation built on May 23, 2017, 11:38 a.m.