F4_C Composition of the CSF compartment in VS-PWH

About the Data

library(dplyr)
library(ggplot2)
library(tidyr)
library(SingleCellExperiment)
library(Seurat)
library(dplyr)
library(SingleR)
library(scuttle)
library(scRNAseq)
library(ggplot2)
library(scales)
library(cowplot)
set.seed(1995)

Load and prepare data

load("data/6.annotated.rda")
FinalFilter <- JoinLayers(FinalFilter)
#SingleR require log norm counts
sce_hayes <- as.SingleCellExperiment(FinalFilter) 
sce_meta <- as.data.frame(colData(hayes.hiv))
#load Cantoni object 
sce_atlas <- readH5AD("data/atlas_raw_counts.h5ad")
sce_atlas <- logNormCounts(
    sce_atlas,
    assay.type = "X",  
    name = "logcounts" 
)

SingleR

res4.singleR <- SingleR(test=sce_hayes, ref=sce_atlas, labels=sce_atlas$cell_type, de.method="wilcox")
#saveRDS(res4.singleR, file = "data/res4.singleR.rds")
#explore results
all.heatmap <- plotScoreHeatmap(res4.singleR)
#heatmap w/both labels
heatmap.both <-plotScoreHeatmap(res4.singleR, annotation_col=as.data.frame(colData(hayes.hiv)[,"cell_types",drop=FALSE]))
#QC plots
plotDeltaDistribution(res4.singleR)
plotScoreDistribution(res4.singleR)

Plot

t1 <- as.data.frame(res4.singleR)
t2 <- t1[, c(8,9,10)]
rownames(t2) <- as.character(rownames(t2))
rownames(FinalFilter@meta.data) <- as.character(rownames(FinalFilter@meta.data))
FinalFilter@meta.data$atlas_labels <- t2[rownames(FinalFilter@meta.data), "labels"]
FinalFilter@meta.data$atlas_labels.pruned <- t2[rownames(FinalFilter@meta.data), "pruned.labels"]
FinalFilter@meta.data$delta.next <- t2[rownames(FinalFilter@meta.data), "delta.next"]
#table for plot
meta <- FinalFilter@meta.data
num_tot_cells <- meta %>%
  dplyr::select(cell_types, atlas_labels, ID) %>%
  dplyr::count(cell_types, atlas_labels, name = "total_cells") %>%
  dplyr::ungroup()
#optional
num_tot_cells <- num_tot_cells %>%
    complete(cell_types, atlas_labels, fill = list(total_cells = 0))

prop_within_celltype <- num_tot_cells %>%
    group_by(cell_types) %>%
    mutate(prop = total_cells / sum(total_cells)) %>%
    ungroup()

ggplot(prop_within_celltype,
       aes(x = cell_types, y = prop, fill = atlas_labels)) +
    geom_col(width = 0.9) +
    scale_y_continuous(labels = percent) +
    labs(x = "Hayes 2025 azimuth based cell type", y = "% cell population label congruence",
         fill = "Cantoni et.al. Labels",
         title = " ") +
    theme_cowplot() +
      theme(
    axis.text.x  = element_text(angle = 90, vjust = 0.5),
    axis.title.x = element_text(margin = margin(t = 20)),  
    axis.title.y = element_text(size = 12),
    legend.text   = element_text(size = 10),   
    legend.title  = element_text(size = 9)
  )

#ggsave("plots/SingleR_bulkALL_congruence.svg", width = 14, height = 10, dpi = 300)

sessionInfo()