featurePlot: Detect the sgRNA distribution and generate Vlnplot to...

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

Detect the sgRNA distribution and generate Vlnplot to identity gene regulation between different cells.

Description

identify how many sgRNAs entered the cells, visualize the counts of sgRNAs distribution and gene regulation.

Usage

featurePlot(RDS, TYPE = plot.type, BARCODE = NULL, sgRNA = NULL, GENE = NULL, CONTROL = NULL, GROUP2=NULL,SLOT='data',palette = NULL, label.size = 3, axis.size = 12, title.size = 15, legend.text = 10, fill = "#56B4E9")

plot.type
# c("Dis", "Vln", "Den")

Arguments

RDS	RDS object from the pre-processRDS step
TYPE	Type of the plot.
BARCODE	A txt file to include cell identity information, generated from the cell identity collection step.
sgRNA	Generate whole sgRNAs distribution when sgRNA = NULL, add sgRNAs to see the specific sgRNA distribution. Mutiple sgRNAs can be provided, separated by ",". For example, "APC,TP53".
GENE	Genes whose expressions are to be compared under different cell coditions. Mutiple genes can be provided, as a vector or as a string separated by ",". For example, "APC,TP53". when provide mutiple genes, it would show the average gene expression.
CONTROL	Set up the sepecifc clusters to compare the gene expression. it would compare gene expression across the dataset when CONTROL = NULL.
GROUP2	Set up the gene name as the control group. it would compare gene expression across the dataset when GROUP2= NULL.
SLOT	Use the slot in Seurat object for plot. May choose 'data', 'scale.data' or 'count'. See GetAssayData funciton in Seurat.
palette	The color palette to change the color of VlnPlot.
label.size	Text size of label.
axis.size	Text size of axis.text.
title.size	Text size of axis/pics' title.
legend.text	Text size of figure legend.
fill	Fill colour.

Examples

    
    ### Loading required package
    require(Seurat)
    
    ### 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")
    
    ### For using the featurePlot function, it needs to do the preprocessRDS first
    Demo <- pre_processRDS(BARCODE = BARCODE, RDS = RDS)
    
    ### For the sgRNA distribution
    featurePlot(BARCODE = BARCODE, RDS = Demo, TYPE = "Dis")
    
    ### For the density of sgRNA, clustering needed to be done first.
    Demo <- RunUMAP(Demo, dims = 1:10)
    featurePlot(RDS = Demo, sgRNA = NULL, TYPE = "Den")
    
    ###For the Vlnplot to display the gene regulation, take MKI67 for examples
    featurePlot(RDS = Demo, GENE = "MKI67", sgRNA = "TP53", TYPE = "Vln")

weililab/scMAGeCK documentation built on April 21, 2024, 10:36 a.m.