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Bubble blot showing the expression or activity of selected features by self-defined groups

feature_bubbleplot.Rd

This function is used to draw a bubble plot of selected features among self-defined groups from a sparse eset object.

Usage

feature_bubbleplot(
  input_eset,
  features = NULL,
  group_by = "clusterID",
  colors = NULL,
  legend.position = "right",
  fontsize.legend_title = 10,
  fontsize.legend_text = 8,
  fontsize.axis_title = 10,
  fontsize.axis_text = 8,
  xlabel.angle = 0
)

Arguments

input_eset

The expression set object that filtered, normalized and log-transformed

features

A vector of genes or drivers (row.names of the input eset) to plot

group_by

Character, name of the column for grouping, usually the column of cell types or clusters. Default: "clusterID".

colors

A vector of two colors indicating the low and high values respectively. Default: c("lightgrey", "red").

legend.position

Character, position of legend: "right" (the default), "left", "top", "bottom" or "none".

fontsize.legend_title

Integer, font size of the legend title. Default: 10.

fontsize.legend_text

Integer, font size of the legend text. Default: 8.

fontsize.axis_title

Integer, font size of the axis label and text. Default: 10.

fontsize.axis_text

Integer, font size of the axis label and text. Default: 8.

xlabel.angle

Numeric, the angle of the a-axis title. When it's set not 0, the x-axis text will automatically right-justified. Default: 0.

Value

A ggplot object of bubble plot

Examples

data(pbmc14k_expression.eset)
features_of_interest <- c("CD3D","CD27","IL7R","SELL","CCR7","IL32","GZMA","GZMK",
                          "DUSP2","CD8A","GZMH","GZMB","CD79A","CD79B","CD86","CD14")
## 1. the most commonly used command
feature_bubbleplot(input_eset = pbmc14k_expression.eset,
                   features = features_of_interest,
                   group_by = "clusterID")


## 2. customize the colors
feature_bubbleplot(input_eset = pbmc14k_expression.eset,
                   features = features_of_interest,
                   group_by = "clusterID",
                   colors = c("lightgrey", "blue"))