scMAGeCK-package {scMAGeCK} | R Documentation |
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)
The DESCRIPTION file:
Package: | scMAGeCK |
Type: | Package |
Title: | Identify genes associated with multiple expression phenotypes in single-cell CRISPR screening data |
Version: | 1.6.0 |
Date: | 2021-10-22 |
Author: | Wei Li, Xiaolong Cheng, Lin Yang |
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, ggplot2, stats, utils |
Suggests: | knitr, rmarkdown |
VignetteBuilder: | knitr |
git_url: | https://git.bioconductor.org/packages/scMAGeCK |
git_branch: | RELEASE_3_14 |
git_last_commit: | 194b6e1 |
git_last_commit_date: | 2021-10-26 |
Date/Publication: | 2021-10-26 |
Index of help topics:
featurePlot Detect the sgRNA distribution and generate Vlnplot to identity gene regulation between different cells. pre_processRDS Integrate the imformation of sgRNA into RDS file for the further analysis. 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 selectPlot generate the selection plot
Further information is available in the following vignettes:
scMAGeCK | scMAGeCK (source, pdf) |
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/).
Wei Li, Xiaolong Cheng, Lin Yang
Maintainer: Xiaolong Cheng <xiaolongcheng1120@gmail.com>
### 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") target_gene <- "MKI67" ### Set RRA executable file path or activate scmageck env if needed (see https://bitbucket.org/weililab/scmageck/src/master/) RRAPATH <- NULL 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)