We’ll demonstrate visualization techniques in Seurat using our previously computed Seurat object from the 2,700 PBMC tutorial. You can download that here

library(Seurat)
library(ggplot2)
pbmc <- readRDS(file = "../data/pbmc3k_final.rds")
pbmc$groups <- sample(c("group1", "group2"), size = ncol(pbmc), replace = TRUE)
features <- c("LYZ", "CCL5", "IL32", "PTPRCAP", "FCGR3A", "PF4")
pbmc
## An object of class Seurat 
## 13714 features across 2638 samples within 1 assay 
## Active assay: RNA (13714 features)
##  2 dimensional reductions calculated: pca, umap

Five visualizations of marker feature expression

# Ridge plots - from ggridges. Visualize single cell expression distributions in each cluster
RidgePlot(pbmc, features = features, ncol = 2)

# Violin plot - Visualize single cell expression distributions in each cluster
VlnPlot(pbmc, features = features)

# Feature plot - visualize feature expression in low-dimensional space
FeaturePlot(pbmc, features = features)