Package 'scatterbar'

Deconvolved cell-type proportions from STdeconvolve for a Visium data from the adult mouse brain along with the positions of their respective spots

Description

Deconvolved cell-type proportions from STdeconvolve for a Visium data from the adult mouse brain along with the positions of their respective spots

Usage

data(adult_mouse_brain_ffpe)

Format

A list of 2 objects, each of 2264 rows and 2 columns: prop, which contains the 12 deconvolved cell-types proportions for each 2264 spots/pixels in a spatial transcriptomics experiment performed on adult mouse brain tissue tissue and pos, which contains the x and y-coordinates for each 2264 spots/pixels on an adult mouse brain tissue slide.

Source

https://www.10xgenomics.com/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0

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Deconvolved cell-type proportions from STdeconvolve for a Spatial Transcriptomic dataset of the mouse olfactory bulb along with the positions of their respective spots

Description

Deconvolved cell-type proportions from STdeconvolve for a Spatial Transcriptomic dataset of the mouse olfactory bulb along with the positions of their respective spots

Usage

data(mOB)

Format

A list of 2 objects, each of 260 rows and 2 columns: data, which contains the 8 deconvolved cell-types proportions for each 260 spots/pixels in a spatial transcriptomics experiment performed on mouse olfactory bulb tissue and xy, which contains the x and y-coordinates for each 260 spots/pixels on a mouse olfactory bulb tissue slide.

Source

https://www.science.org/doi/10.1126/science.aaf2403

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Create a scattered stacked bar chart plot

Description

This function creates a scatterbar plot using ggplot2, where the bars are stacked based on the different proportions of groups in each 2-D location/spot. A scatterbar plot is a combination of a scatter plot and a stacked bar chart, allowing for the visualization of proportional data across spatial coordinates. The function allows for customized scaling factors and padding when creating the plot. If no scaling factors are specified, the function automatically determines the optimal scaling factors based on the data.

Usage

scatterbar(
  data,
  xy,
  size_x = NULL,
  size_y = NULL,
  padding_x = 0,
  padding_y = 0,
  show_legend = TRUE,
  legend_title = "Group",
  colors = NULL,
  verbose = TRUE
)

Arguments

data	A data frame containing the proportions of different categories for each location. Each row represents a location, and each column (except the row names) represents a category.
xy	A data frame containing the positional information for each location. This data frame includes the x and y coordinates for each location/spot (the respective row names).
size_x	X-axis scaling factor (default is NULL). If not provided, it will be automatically calculated based on the data.
size_y	Y-axis scaling factor (default is NULL). If not provided, it will be automatically calculated based on the data.
padding_x	Padding for x-axis (default is 0).
padding_y	Padding for y-axis (default is 0).
show_legend	Boolean indicating whether to display the plot legend (default is TRUE).
legend_title	Custom title for the legend (default is "Group").
colors	Optional vector of colors to use for each category (default is NULL). If not provided, a default palette will be used.
verbose	Boolean indicating whether to print the calculated scaling and padding values (default is TRUE).

Value

A ggplot object representing the scattered stacked bar chart plot.

Examples

data(mOB)
scatterbar(mOB$data, mOB$xy, padding_x = 0.3, padding_y = 0.3, legend_title = "Cell Types")

data(adult_mouse_brain_ffpe)
scatterbar(
adult_mouse_brain_ffpe$prop,
adult_mouse_brain_ffpe$pos,
size_x = 220,
size_y = 220,
legend_title = "Cell Types")

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