GeneScatterPlot {ILoReg} | R Documentation

## Visualize gene expression over nonlinear dimensionality reduction

### Description

GeneScatterPlot enables visualizing gene expression of a gene over nonlinear dimensionality reduction with t-SNE or UMAP.

### Usage

    GeneScatterPlot.SingleCellExperiment(
      object,
      genes,
      return.plot,
      dim.reduction.type,
      point.size,
      title,
      plot.expressing.cells.last,
      nrow,
      ncol
    )

    ## S4 method for signature 'SingleCellExperiment'
    GeneScatterPlot(
      object,
      genes = "",
      return.plot = FALSE,
      dim.reduction.type = "tsne",
      point.size = 0.7,
      title = "",
      plot.expressing.cells.last = FALSE,
      nrow = NULL,
      ncol = NULL
    )

### Arguments

`object` | of `SingleCellExperiment` class
`genes` | a character vector of the genes to be visualized
`return.plot` | whether to return the ggplot2 object or just draw it (default `FALSE`)
`dim.reduction.type` | "tsne" or "umap" (default "tsne")
`point.size` | point size (default 0.7)
`title` | text to write above the plot
`plot.expressing.cells.last` | whether to plot the expressing genes last to make the points more visible
`nrow` | a positive integer that specifies the number of rows in the plot grid. Default is `NULL`.
`ncol` | a positive integer that specifies the number of columns in the plot grid. Default is `NULL`.

### Value

ggplot2 object if return.plot=TRUE

### Examples

    library(SingleCellExperiment)
    sce <- SingleCellExperiment(assays = list(logcounts = pbmc3k_500))
    sce <- PrepareILoReg(sce)
    ## These settings are just to accelerate the example, use the defaults.
    sce <- RunParallelICP(sce,L=2,threads=1,C=0.1,k=5,r=1)
    sce <- RunPCA(sce,p=5)
    sce <- RunTSNE(sce)
    GeneScatterPlot(sce,"CD14",dim.reduction.type="tsne")
    sce <- RunUMAP(sce)
    GeneScatterPlot(sce,"CD14",dim.reduction.type="umap")

[Package ILoReg version 1.2.0 Index]