Lymph Node Single-Cell Cluster Annotation Part I: Expression of Key Markers
Eileen Owens
2025-01-31
Introduction
The purpose of this script is to annotate single-cell RNA-seq clusters following filtering, normalization, and clustering of the data with Seurat. This script has been adapted from the Seurat (https://satijalab.org/seurat/) documentation.
Software
library(Seurat)
library(tidyverse)
library(patchwork)
library(pheatmap)
library(RColorBrewer)
library(data.table)
library(knitr)
library(clusterProfiler)
library(presto)Data
Input: Pre-processed Seurat object.
setwd("/scratch/alpine/edlarsen@colostate.edu/project_scrna_01/240828_scRNAseq/Cluster_Annotation")
ln_seurat <- readRDS(file = "../Normalization_and_Clustering/LN_NormalizedAndClustered.RData")Differentially Expressed Features
By default, identifies all positive and negative markers of a single cluster compared to all other cells.
ln_seurat <- FindClusters(ln_seurat, resolution = 0.1)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 29438
## Number of edges: 895159
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9696
## Number of communities: 9
## Elapsed time: 6 secondsln_seurat <- RunUMAP(ln_seurat, dims = 1:10)
ln.markers <- FindAllMarkers(ln_seurat, only.pos=TRUE)
# export
write.csv(ln.markers, file="LN_DEfeaturesByUMAPcluster_res0.1.csv")
# umap
DimPlot(ln_seurat,
reduction = "umap",
label = TRUE,
label.size = 6) +
plot_annotation(title = "Canine Lymph Node, Resolution: 0.1", theme = theme(plot.title = element_text(hjust = 0.5, size = 20)))
Top cluster markers
key.genes <- ln.markers[!grepl("ENSCAFG", row.names(ln.markers)),]
key.genes.sortedByPval <- key.genes[order(key.genes$p_val),]
features <- key.genes.sortedByPval %>% group_by(cluster) %>% do(head(., n=5))
features <- as.data.frame(features[!duplicated(features$gene),]) # prioritize unique clusters
DotPlot(ln_seurat,
assay = "RNA",
features = rev(features$gene),
scale = TRUE) +
geom_point(aes(size=pct.exp), shape = 21, colour="black", stroke=0.5) +
labs(y = "Cluster") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Feature plots
Some of these markers were derived from a single-cell atlas of circulating canine leukocytes (https://github.com/dyammons/Canine_Leukocyte_scRNA).
FeaturePlot(ln_seurat,
reduction = "umap",
ncol = 3,
features = c("CD3E", "CD5", "CD4", "FOXP3", "IL2RA", "GATA3", "TBX21", "RORC")) +
plot_annotation(title = "Canine Lymph Node: Expression of Canonical CD4 T-cell Markers", theme = theme(plot.title = element_text(hjust = 0.5, size = 20))) &
scale_color_gradientn(colours = brewer.pal(name = "RdPu", n=11))
FeaturePlot(ln_seurat,
reduction = "umap",
features = c("GNLY", "NKG7", "NCAM1", "KLRK1", "CD8A", "CD3E", "CD5"),
ncol = 3) +
plot_annotation(title = "Canine Lymph Node: Expression of Canonical CD8 & NK Cell Markers", theme = theme(plot.title = element_text(hjust = 0.5, size = 20))) &
scale_color_gradientn(colours = brewer.pal(name = "RdPu", n=11))
FeaturePlot(ln_seurat,
reduction = "umap",
features = c("CCR7", "SELL", "HOPX"),
ncol = 3) +
plot_annotation(title = "Canine Lymph Node: Expression of Naive T-cell Markers", theme = theme(plot.title = element_text(hjust = 0.5, size = 20))) &
scale_color_gradientn(colours = brewer.pal(name = "RdPu", n=11))
FeaturePlot(ln_seurat,
reduction = "umap",
features = c("ICOS", "IL2RA", "LTB", "CTLA4", "LGALS3", "MAF"),
ncol = 3) +
plot_annotation(title = "Canine Lymph Node: Expression of Activated T-cell Markers", theme = theme(plot.title = element_text(hjust = 0.5, size = 20))) &
scale_color_gradientn(colours = brewer.pal(name = "RdPu", n=11))
FeaturePlot(ln_seurat,
reduction = "umap",
features = c("MS4A1", "CD19", "PAX5", "IRF4")) +
plot_annotation(title = "Canine Lymph Node: Expression of Canonical B-cell Markers", theme = theme(plot.title = element_text(hjust = 0.5, size = 20))) &
scale_color_gradientn(colours = brewer.pal(name = "RdPu", n=11))
FeaturePlot(ln_seurat,
reduction = "umap",
ncol = 3,
features = c("LYZ", "FCER1A", "ITGAM", "CD163", "CD68")) +
plot_annotation(title = "Canine Lymph Node: Expression of Canonical Monocyte/Macrophage/DC Markers", theme = theme(plot.title = element_text(hjust = 0.5, size = 20))) &
scale_color_gradientn(colours = brewer.pal(name = "RdPu", n=11))
FeaturePlot(ln_seurat,
reduction = "umap",
features = c("FCER1A", "MMP9", "CCR5", "CXCR2", "EPX", "MMP8", "S100A12", "SERPINA1"),
ncol = 3) +
plot_annotation(title = "Canine Lymph Node: Expression of Canonical Granulocyte Markers", theme = theme(plot.title = element_text(hjust = 0.5, size = 20))) &
scale_color_gradientn(colours = brewer.pal(name = "RdPu", n=11))
Note: Granulocytes are difficult to capture with single-cell RNA-seq
experiments given their relatively low RNA content and relatively high
levels of RNAses and other inhibitory compounds, resulting in fewer
detectable transcripts.
Citations
sessionInfo()## R version 4.4.1 (2024-06-14)
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## attached base packages:
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## other attached packages:
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