Molecule Rank Plot

Plots the number of molecules per component against the molecule rank (descending order).

Usage

MoleculeRankPlot(object, ...)

# S3 method for class 'data.frame'
MoleculeRankPlot(
  object,
  group_by = NULL,
  n_umi_min_threshold = NULL,
  n_umi_max_threshold = NULL,
  highlight_cell_counts = TRUE,
  rug = FALSE,
  split = FALSE,
  ...
)

# S3 method for class 'Seurat'
MoleculeRankPlot(
  object,
  group_by = NULL,
  n_umi_min_threshold = NULL,
  n_umi_max_threshold = NULL,
  highlight_cell_counts = TRUE,
  rug = FALSE,
  split = FALSE,
  ...
)

Arguments

object

A data.frame-like object or a Seurat object

...

Parameters passed to other methods

group_by

A character specifying a column to group by. By default, the groups are assigned a unique color. If split = TRUE, the points are not colored.

n_umi_min_threshold, n_umi_max_threshold

Minimum/maximum number of UMIs to define a component as "Normal". If provided, the components will be grouped into categories "Low", "Normal" and "High" based on the number of UMIs and the provided thresholds.

highlight_cell_counts

Whether to highlight cell counts for categories "Low", "Normal" and "High".

rug

Whether to add a rug plot on the left side of the plot to highlight the component density.

split

Whether to split the plot by group_by into facets.

Value

A ggplot object

Examples

library(pixelatorR)

# Load example data as a Seurat object
pxl_file_pna <- minimal_pna_pxl_file()

seur_obj_pna <- ReadPNA_Seurat(pxl_file_pna)
#> ✔ Created a <Seurat> object with 5 cells and 158 targeted surface proteins
seur_obj_pna
#> An object of class Seurat 
#> 158 features across 5 samples within 1 assay 
#> Active assay: PNA (158 features, 158 variable features)
#>  2 layers present: counts, data

# Plot with data.frame
MoleculeRankPlot(seur_obj_pna[[]])


library(pixelatorR)

# Plot with Seurat object
MoleculeRankPlot(seur_obj_pna)