DESCRIPTION

Package: siMEM
Title: Prediction of differential gene essentiality from pooled phenotypic screens
        (eg: siRNA, shRNA, CRISPR/Cas9 screens) using linear mixed effect models.
Version: 1
Authors@R: person("Azin", "Sayad", email = "asayad@gmail.com", role = c("aut", "cre"))
Description: 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.
Imports:
        Biobase,
        genefilter,
        preprocessCore,
        blme,
        doMC,
        foreach,
        ggplot2,
        locfit,
        MASS,
        plyr,
        reshape
License: MIT
LazyData: true