Macrophages Subpopulation Analysis

This section focuses on a detailed sub-analysis of the macrophages population. We subset the macrophages cells from the main dataset, reclustered them to identify distinct macrophages subtypes.

1 Setup and Environment Configuration

suppressPackageStartupMessages({
  library(Seurat)
  library(SeuratWrappers)
  library(tidyverse)
  library(scCustomize)
  library(CellChat)
  library(slurmR)
  library(tidyverse)
  library(reticulate)
  library(qs)
  library(clustree)
  library(org.Hs.eg.db)
  library(Seurat)
  library(slurmR)
  library(cowplot)
  library(tidyverse)
  library(pheatmap)
  library(qs)
})

ref <- qread("../outputs/scRNAseq_Analysis/scRef.qs")

2 Subset and Re-cluster Macrophages

macs <- subset(ref, subset = main_annotation_scvi == 'Macrophages')
macs <- RunUMAP(macs, reduction = 'integrated.scvi', dims = 1:30)
macs <- FindNeighbors(macs, reduction = 'integrated.scvi', dims = 1:30)
res <- seq(from = 0.05, to = 1, by = 0.05)
for(x in res){
  macs <- FindClusters(macs, resolution = x, cluster.name = paste0("res.",x))
}
clustree(macs, prefix = 'res.', layout = "sugiyama")
macs <- FindClusters(macs, resolution = 0.35)

3 Annotate Macrophages Subtypes

DimPlot_scCustom(macs)
FeaturePlot(macs, features = c("CD14", 'APOE', 'MARCO', 'CCR2', 'CD68', 'C1QA', 'FCGR3A', 'FCGR3B', 'SPP1', 'IL1B', 'HBEGF', 'VEGFA'))
Idents(macs) <- macs$seurat_clusters

markers <- FindAllMarkers(macs)
markers$scores <- markers$avg_log2FC * markers$pct.1

DotPlot_scCustom(macs, features = unique(top_markers), group.by = 'seurat_clusters', flip_axes = T) +
  theme(axis.text.y = element_text(size = 9, face = 'bold'),
        axis.text.x = element_text(size = 9, face = 'bold'))

new_idents <- c('0' = 'Classical_Macs',
                '1' = 'Resident_Macs',
                '2' = 'THBS1+_Macs',
                '3' = 'AltAct_Macs',
                '4' = 'AltAct_Macs',
                '5' = 'AltAct_Macs',
                '6' = 'Resident_Macs',
                '7' = 'Classical_Macs',
                '8' = 'Nonclassical_Macs',
                '9' = 'AltAct_Macs',
                '10' = 'Dendritic',
                '11' = 'Cycling_Macs',
                '12' = 'Nonclassical_Macs',
                '13' = 'T_Cells_Doublets',
                '14' = 'RBCs')

macs <- RenameIdents(macs, new_idents)
macs$macs_subtypes <- Idents(macs)

macs <- macs[, !(macs$macs_subtypes %in% c("T_Cells_Doublets", "RBCs"))]
macs <- RunUMAP(macs, reduction = 'integrated.scvi', dims = 1:30)

macs$macs_subtypes <- factor(macs$macs_subtypes, levels = c("Classical_Macs", 
                                                            "Nonclassical_Macs",
                                                            "Resident_Macs", 
                                                            "AltAct_Macs", 
                                                            "Cycling_Macs",
                                                            "THBS1+_Macs",
                                                            "Dendritic"))

markers <- FindAllMarkers(macs, group.by = 'macs_subtypes')
markers$scores <- markers$avg_log2FC * markers$pct.1
macs@misc$markers <- markers

qsave(macs, "../outputs/scRNAseq_Analysis/scRef_macs.qs")

ref <- qread("../outputs/scRNAseq_Analysis/scRef_macs.qs")

DimPlot2(macs)

features <- c("S100A8", "S100A9", "LYZ", "FCN1",
              "FCGR3A", "IFITM2", "IFITM3",
              "C1QA", "C1QB", "CCL4",
              "APOE", "MARCO", "SPP1",
              "MKI67","TOP2A",
              "THBS1","AREG","EREG",
              "HLA-DRA", "CD74", "CLEC9A", "IDO1")

DotPlot2(macs, features = features, group.by = 'macs_subtypes', color_scheme = 'RdYlBu-rev', flip = T) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

sessionInfo()