Output one or multiple featureplots

Usage

TS_featureplots(
  seurat_object = get(default_seurat_object),
  features,
  wrap_plots = TRUE,
  wrap_n_columns = NULL,
  add_dimplot = TRUE,
  dimplot_group.by = NULL,
  dimplot_cols = NULL,
  dimplot_label = TRUE,
  expression_cols = c("lightgray", "#FEFDE2", "#FEEAC7", "#FDD7AC", "#FDC591", "#FDB276",
    "#FC9F5B", "#FC8C40", "#FC8C40", "#EB753C", "#DA5D38", "#C94634", "#B72F30",
    "#A6172C", "#950028"),
  reduction = "umap",
  pt.size = 1,
  order = TRUE,
  assay = NULL,
  reverse_y_scale = FALSE,
  reverse_x_scale = FALSE
)

Arguments

seurat_object: The Seurat object you wish to plot. Defaults to "default_seurat_object".
features: Vector of features to plot.
wrap_plots: Whether to wrap plots into one by patchwork::wrap_plots(). Defaults to TRUE.
wrap_n_columns: If wrap_plots = T, how many columns do you want for the final plot.
add_dimplot: If wrap_plots = T, whether to include Seurat::DimPlot() as the first panel. Defaults to TRUE
dimplot_group.by: What metadata to group cells by. Defaults to Seurat::Idents(seurat_object)
dimplot_cols: Colours to use for the Seurat::DimPlot(). Defaults to scales::hue_pal()(length(levels(Seurat::Idents(seurat_object))))
dimplot_label: Whether to label the DimPlot by dimplot_group.by. Defaults to TRUE.
expression_cols: Vector of colors to use (low->high), defaults to a 15-step gradient from "lightgray" to "#950028"
reduction: Dimensionality reduction technique to use for plotting, default = "umap".
pt.size: Size of each dot, default = 1.
order: Whether to plot positive cells last, e.g. on top, default = TRUE.
assay: What assay to pull gene expression data from. Defaults to "RNA", as this is the convention.
reverse_y_scale: Logical. Should the Y axis be reversed?
reverse_x_scale: Logical. Should the X axis be reversed?