Dimensionality reduction plot — dimensionality_reduction

Generates the dimensionality reduction plots (UMAP or tSNE) based on marker intensities. Cells are grouped by the categories under the selected column. -- have you tried doing PCA on the matrix and then doing the UMAP/tSNE? Does it help? scRNAseq workflows do this. (?)

Usage

dimensionality_reduction_plot(
  sce_object,
  plot_type = "UMAP",
  scale = TRUE,
  feature_colname
)

Arguments

sce_object: SingleCellExperiment object in the form of the output of format_image_to_sce.
plot_type: String. Choose from "UMAP" and "TSNE".
scale: Boolean. Whether scale the marker intensities.
feature_colname: String. Specify the column name to group the cells.

Value

A plot

Examples

dimensionality_reduction_plot(SPIAT::simulated_image, plot_type = "TSNE", 
feature_colname = "Phenotype")