Generating Cluster Masks for Single-Cell Dimensional Reduction Plots • mascarade

mascarade package implements a procedure to automatically generate 2D masks for clusters on single-cell dimensional reduction plots like t-SNE or UMAP.

See the tutorial for usage details and gallery for examples on different datasets.

Installation

The package can be installed from GitHub:

remotes::install_github("alserglab/mascarade")

Quick run

Loading necessary libraries:

library(mascarade)
library(ggplot2)
library(data.table)
library(Seurat)

Loading get the example PBMC3K dataset:

pbmc3k <- readRDS(url("https://alserglab.wustl.edu/files/mascarade/examples/pbmc3k_seurat5.rds"))
pbmc3k <- NormalizeData(pbmc3k)
pbmc3k

## An object of class Seurat 
## 13714 features across 2638 samples within 1 assay 
## Active assay: RNA (13714 features, 2000 variable features)
##  2 layers present: counts, data
##  2 dimensional reductions calculated: pca, umap

Generating masks:

maskTable <- generateMaskSeurat(pbmc3k)

DimPlot with the mask and labels:

DimPlot(pbmc3k) + NoLegend() +
    fancyMask(maskTable, ratio=1)

DimPlot with the just the labels

DimPlot(pbmc3k) + NoLegend() +
    fancyMask(maskTable, linewidth=0, ratio=1)

FeaturePlot with the mask and labels showing GNLY gene being specific to NK cells:

FeaturePlot(pbmc3k, "GNLY", cols=c("grey90", "red")) +
    fancyMask(maskTable, ratio=1)