Chapter 4 Scrublet Doublet Validation

library(Seurat)
library(tidyverse)
library(magrittr)
library(data.table)

4.1 Description

  • check the doublet prediction from scrublet by

    • dimension reduction plot

    • nUMI distribution

  • judge the component for doublet cells by

    • DEG heatmap

    • canonical gene expression

4.2 Load seurat object

combined <- get(load('data/Demo_CombinedSeurat_SCT_Preprocess.RData'))
Idents(combined) <- "cluster"

4.3 Validate the doublet prediction

# check whether the double cells cluster together
FeaturePlot(combined, features = "DoubletScores", pt.size = 0.01)

DimPlot(
  combined,
  group.by = "DoubletPrediction",
  pt.size = 0.01,
  cols = c("red", "azure3")
)

# check the nUMI for doublet and singlet
VlnPlot(combined,
        features = "nCount_RNA",
        pt.size = 0,
        group.by = "DoubletPrediction") + NoLegend()

4.4 Calculate factions of doublet per cluster

df <- data.table(combined@meta.data)
sel.meta <- c("DoubletPrediction", "cluster", "Individual")
df <- df[, sel.meta, with = FALSE]


df[, 2:3] %>% map( ~ {
  freq1 <- df[, .N, keyby = .(.x, DoubletPrediction)]
  freq1[, total := sum(N), by = .(.x)]
  freq1[, ratio := N / total]
  
  linesize = .35
  fontsize = 8

  ggplot(freq1, aes(fill=DoubletPrediction, y=ratio, x= .x)) + 
    geom_bar(position="stack", stat="identity")+
    scale_fill_manual(values = c("Doublet" = 'red', "Singlet" = "grey")) +
    xlab('Clsuter') +
    scale_y_continuous(breaks = seq(0,1,0.1), expand = c(0,0), name = 'Percentage')+
    theme_bw()+
    theme( panel.grid.major.x = element_blank(), 
           panel.grid.major.y = element_blank(),
           panel.grid.minor = element_blank(),
           strip.background = element_blank(),panel.border = element_rect(size = linesize),
           axis.ticks = element_blank(), 
           axis.text.x = element_text(size = 5))
  
})
## $cluster

## 
## $Individual

4.5 Explore the component clusters for doublets by DEG

  • get the DEG for inferred source clusters. Here, for C33, InCGE and InMGE
# find DEG
cluster.markers <- FindMarkers(combined, ident.1 = c("InMGE"), ident.2 = "InCGE", min.pct = 0.25)

# subset cells of interest
sel.idents <- c("InMGE", "InCGE",  "D33")
combined.small <- subset(combined, cells = WhichCells(combined, idents = sel.idents))

# check the expression for top DEG
#sel.cells <- WhichCells(combined.small, idents = sel.idents, downsample =  355) # for large dataset 
DoHeatmap(combined.small, features = rownames(cluster.markers)[1:40], raster = F)

4.6 Explore the component clusters for doublets by canonical gene

sel.feature <- c("NXPH1", "PAM", "LHX6", "NR2F2", "ADARB2",  "PROX1")
FeaturePlot(combined, features = sel.feature,  pt.size = 0.01, ncol = 3)

VlnPlot(combined.small, features = sel.feature, pt.size = 0, ncol = 3, idents = sel.idents)