plotTSCANClusterDEG: Plot features identified by 'runTSCANClusterDEAnalysis' on...

In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Plot features identified by runTSCANClusterDEAnalysis on cell 2D embedding with MST overlaid

Description

A wrapper function which plot the top features expression identified by runTSCANClusterDEAnalysis on the 2D embedding of the cells cluster used in the analysis. The related MST edges are overlaid.

Usage

plotTSCANClusterDEG(
  inSCE,
  useCluster,
  pathIndex = NULL,
  useReducedDim = "UMAP",
  topN = 9,
  useAssay = NULL,
  featureDisplay = metadata(inSCE)$featureDisplay,
  combinePlot = c("all", "none")
)

Arguments

inSCE	Input SingleCellExperiment object.
useCluster	Choose a cluster used for identifying DEG with runTSCANClusterDEAnalysis. Required.
pathIndex	Specifies one of the branching paths from useCluster and plot the top DEGs on this path. Ususally presented by the terminal cluster of a path. By default NULL plot top DEGs of all paths.
useReducedDim	A single character for the matrix of 2D embedding. Should exist in reducedDims slot. Default "UMAP".
topN	Integer. Use top N genes identified. Default 9.
useAssay	A single character for the feature expression matrix. Should exist in assayNames(inSCE). Default NULL for using the one used in runTSCANClusterDEAnalysis.
featureDisplay	Specify the feature ID type to display. Users can set default value with setSCTKDisplayRow. NULL or "rownames" specifies the rownames of inSCE. Other character values indicates rowData variable.
combinePlot	Must be either "all" or "none". "all" will combine plots of each feature into a single .ggplot object, while "none" will output a list of plots. Default "all".

Value

A .ggplot object of cell scatter plot, colored by the expression of a gene identified by runTSCANClusterDEAnalysis, with the layer of trajectory.

Author(s)

Yichen Wang

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runTSCAN(inSCE = mouseBrainSubsetSCE,
                                useReducedDim = "PCA_logcounts")
mouseBrainSubsetSCE <- runTSCANClusterDEAnalysis(inSCE = mouseBrainSubsetSCE,
                                                 useCluster = 1)
plotTSCANClusterDEG(mouseBrainSubsetSCE, useCluster = 1,
                    useReducedDim = "TSNE_logcounts")

compbiomed/singleCellTK documentation built on Oct. 27, 2024, 3:26 a.m.