| 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)