scMAGeCK-package {scMAGeCK}R Documentation

Identify genes associated with multiple expression phenotypes in single-cell CRISPR screening data

Description

scMAGeCK is a computational model to identify genes associated with multiple expression phenotypes from CRISPR screening coupled with single-cell RNA sequencing data (CROP-seq)

Details

The DESCRIPTION file:

Package: scMAGeCK
Type: Package
Title: Identify genes associated with multiple expression phenotypes in single-cell CRISPR screening data
Version: 1.0.0
Date: 2019-12-13
Author: Wei Li, Xiaolong Cheng
Maintainer: Xiaolong Cheng <xiaolongcheng1120@gmail.com>
Description: scMAGeCK is a computational model to identify genes associated with multiple expression phenotypes from CRISPR screening coupled with single-cell RNA sequencing data (CROP-seq)
License: BSD_2_clause
biocViews: CRISPR, SingleCell, RNASeq, PooledScreens, Transcriptomics, GeneExpression, Regression
NeedsCompilation: yes
Imports: Seurat, stats, utils
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scMAGeCK
git_branch: RELEASE_3_11
git_last_commit: 177099a
git_last_commit_date: 2020-04-27
Date/Publication: 2020-04-27

Index of help topics:

scMAGeCK-package        Identify genes associated with multiple
                        expression phenotypes in single-cell CRISPR
                        screening data
scmageck_lr             Use linear regression to test the association
                        of gene knockout with all possible genes
scmageck_rra            Use RRA to test the association of gene
                        knockout with certain marker expression

scMAGeCK is a computational model to identify genes associated with multiple expression phenotypes from CRISPR screening coupled with single-cell RNA sequencing data (CROP-seq).scMAGeCK is based on our previous MAGeCK and MAGeCK-VISPR models for pooled CRISPR screens.

The scMAGeCK manuscript can be found at bioRxiv(https://www.biorxiv.org/content/10.1101/658146v1/).

Author(s)

Wei Li, Xiaolong Cheng

Maintainer: Xiaolong Cheng <xiaolongcheng1120@gmail.com>

Examples

    ### BARCODE file contains cell identity information, generated from 
    ### the cell identity collection step
    BARCODE <- system.file("extdata","barcode_rec.txt",package = "scMAGeCK")
    ### RDS can be a Seurat object or local RDS file path that contains 
    ### the scRNA-seq dataset
    RDS <- system.file("extdata","singles_dox_mki67_v3.RDS",package = "scMAGeCK")
    
    ### Set RRA executable file path. 
    ###   You can generate RRA executable file by following commands:
    ###   wget https://bitbucket.org/weililab/scmageck/downloads/RRA_0.5.9.zip
    ###   unzip RRA_0.5.9.zip
    ###   cd RRA_0.5.9
    ###   make
    RRAPATH <- "/Library/RRA_0.5.9/bin/RRA"
    
    target_gene <- "MKI67"
    
    rra_result <- scmageck_rra(BARCODE=BARCODE, RDS=RDS, GENE=target_gene, 
                               RRAPATH=RRAPATH, LABEL='dox_mki67', 
                               NEGCTRL=NULL, KEEPTMP=FALSE, 
                               PATHWAY=FALSE, SAVEPATH=NULL)
    head(rra_result)
    
    lr_result <- scmageck_lr(BARCODE=BARCODE, RDS=RDS, LABEL='dox_scmageck_lr', 
            NEGCTRL = 'NonTargetingControlGuideForHuman', PERMUTATION = 1000,
            SAVEPATH=NULL, LAMBDA=0.01)
    lr_score <- lr_result[1]
    lr_score_pval <- lr_result[2]
    head(lr_score_pval)

[Package scMAGeCK version 1.0.0 Index]