inst/doc/scMAGeCK.R

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

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## -----------------------------------------------------------------------------
    library(scMAGeCK)
    ### 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)
    

## -----------------------------------------------------------------------------
    library(scMAGeCK)
    ### 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")
    
    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]