feature_plot_tailored: Tailored feature plot

In andreaskapou/SeuratPipe: Packaging common Seurat analysis tasks

Tailored feature plot

Description

This function generates the same plot as feature_plot, although it focuses on a single feature and generates slightly better looking plot.

Usage

feature_plot_tailored(
  seu,
  feature,
  max.cutoff = "q98",
  min.cutoff = NA,
  reduction = "umap",
  slot = "data",
  col_pal = NULL,
  legend.position = "right",
  pt.size = 2,
  pt.shape = 21,
  pt.stroke = 0.05,
  pt.alpha = 1,
  dims_plot = c(1, 2),
  order_points_by_value = TRUE,
  ...
)

Arguments

seu	Seurat object
feature	Feature to plot.
max.cutoff	Maximum cutoff value for feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10').
min.cutoff	Minimum cutoff value for feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10').
reduction	Dimensionality reduction to use.
slot	Slot to extract data from.
col_pal	Continuous colour palette to use, default "RdYlBu".
legend.position	Position of legend, default "right" (set to "none" for clean plot).
pt.size	Adjust point size for plotting.
pt.shape	Adjust point shape for plotting.
pt.stroke	Stroke value for each point.
pt.alpha	Adjust alpha value for each point.
dims_plot	Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions.
order_points_by_value	Logical, should points be ordered by their value (e.g. expression levels), which corresponds to plotting on top cells that have high expression, instead of getting 'buried' by lowly expressed cells.
...	Additional parameters passed to ggplot2::geom_point.

Value

A ggplot2 object.

Author(s)

C.A.Kapourani [e-mail]

andreaskapou/SeuratPipe documentation built on Nov. 22, 2022, 4:16 p.m.