6.Annotation & Abundance

Author

Lindsay Hayes & Felix Asare

Annotate Cell Types

DimPlot(FinalFilter, reduction = "umap.mnn", group.by = "mnn_clusters", label = T)
ggsave("plots/2.FastMNN/umap.svg", width = 5, height = 5)

GOI <- c("NCAM1", "KLRD1", "GNLY","CD3E", "CD4", "CD8A", "FOXP3","CD79A", "MS4A1", "IGHM", "C1QA","CD14","HEXB", "CSF1R", "AIF1", "CALD1", "S100A13", "KCNMA1")
DotPlot(FinalFilter, features = GOI) + coord_flip()
ggsave("plots/2.FastMNN/dot.svg", width = 8, height = 5)

# Rename the levels of the factor
FinalFilter$cell_types <- FinalFilter$mnn_clusters
cell_types <- c("CD4", "CD8", "CD8", "Myeloid", "CD8", "CD4", "CD4", "Myeloid", "NK", "Myeloid", "Bcell", "Mesenchymal", "CD8")
levels(FinalFilter@meta.data$cell_types) <- cell_types
table(FinalFilter$cell_types)

#Visualize

DimPlot(FinalFilter, reduction = "umap.mnn", group.by = "cell_types", cols = c("seagreen3", "lightgoldenrod", "steelblue2","hotpink", "purple", "grey"))
ggsave("plots/3.clusters/umap.svg", width = 6, height = 5)

CellType Abundance

tmp <-table(FinalFilter$cell_types)
round(prop.table(tmp)*100,1)

table(FinalFilter$cell_types, FinalFilter$ID)
tmp <-table(FinalFilter$cell_types, FinalFilter$ID)
round(prop.table(tmp, margin = 2)*100,1)

abundance <- as.data.frame(round(prop.table(tmp, margin = 2)*100,1))
colnames(abundance) <- c("celltype", "ID", "Freq")

ggplot(abundance, aes(fill=celltype, y=Freq, x=ID)) + 
  geom_bar(position="stack", stat="identity") + theme_cowplot() +
  scale_fill_manual(values = c("steelblue2", "lightgoldenrod","seagreen3","hotpink", "purple", "grey"))
ggsave("plots/3.clusters/stackedbar.svg", width = 5, height = 6)

save(FinalFilter, file = "data/6.annotated.rda")

TidyDat <- JoinLayers(FinalFilter)
TidyDat

Identify celltype enriched genes

Idents(TidyDat) <- TidyDat$cell_types
cluster_markers <- FindAllMarkers(TidyDat,
                                  logfc.threshold = 0.5,
                                  test.use = "roc",
                                  min.pct = 0.25,
                                  only.pos = TRUE)
table(cluster_markers$cluster, dnn = "roc")
write.csv(cluster_markers, file = "writes/cluster_markers_roc_251124.csv")

Heatmap

# Manually curate a list of top cluster marker genes
heatmap_genes <- read.csv("writes/cluster_markers_roc_heatmap.csv")

GOI <- heatmap_genes$gene
TidyDat <- ScaleData(TidyDat, features = GOI)

DotPlot(TidyDat, features = GOI, cols = c("gray90", "black"), group.by = "cell_types") + coord_flip() + 
  scale_color_gradient2(
  low = "white",    
  mid = "white",     
  high = "black",    
  midpoint = 0,      
  limits = c(-1,2.5))

ggsave("plots/3.clusters/cluster_heatmap.svg", width = 7, height = 12)

sessionInfo()