featurePlot {scMAGeCK} | R Documentation

## 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, 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, 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.
`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")

[Package scMAGeCK version 1.6.0 Index]