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: | 1.2.0
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_12
git_last_commit: | d710baa
git_last_commit_date: | 2020-10-27
Date/Publication: | 2020-10-27

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 1.2.0 Index]