Last updated: 2024-05-23

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Introduction

This RMarkdown contains exploratory plots to determine the quality of scRNA-seq data obtained from healthy tissue samples from five key locations of the respiratory system in children. The aim of this project is to develop a cell atlas of the healthy pediatric airway.

Load libraries

suppressPackageStartupMessages({
  library(BiocStyle)
  library(tidyverse)
  library(here)
  library(glue)
  library(RColorBrewer)
  library(scran)
  library(scater)
  library(Seurat)
  library(scuttle)
  library(janitor)
  library(cowplot)
  library(patchwork)
  library(scales)
  library(Homo.sapiens)
  library(msigdbr)
  library(EnsDb.Hsapiens.v86)
  library(ensembldb)
})

Load data

List the batches.

Batch 1- Nasal brushings (n=16)
Batch 2- Tonsils (n=16)
Batch 3- Adenoids (n=16)
Batch 4- Bronchial brushings (n=16)
Batch 5- Nasal brushings (n=16)
Batch 6- BAL (n=8*2)
Batch 7- Bronchial brushings (n=16)
Batch 8- Adenoids (n=16)
Batch 9- Tonsils (n=16)

batches_beforeQC <- dir(here("output/RDS/AllBatches_filtered_SCEs/"), full.names = TRUE, pattern = "_filtered.SCE.rds")
batches_afterQC <- dir(here("output/RDS/AllBatches_Azimuth_SEUs/"), full.names = TRUE, pattern = "filter.Azimuth.SEU.rds")

batches <- data.frame(Before = batches_beforeQC, After = batches_afterQC)

Non-empty droplets from CellRanger

Read CellRanger filtered output for each batch and summarize them together using bar plot.

plot_cellnum <- function(sample_data, title) {
  num_unique_samples <- length(unique(sample_data$Sample))
  color_palette <- colorRampPalette(brewer.pal(min(9, num_unique_samples), "Set3"))(num_unique_samples)
  names(color_palette) <- sort(unique(sample_data$Sample))

  p <- ggplot(sample_data, aes(x = Sample, fill = Sample)) + 
    geom_bar(stat = "count") + 
    coord_flip() + 
    labs(y = "Number of droplets", title = title) +
    theme_cowplot(font_size = 10) +
    geom_text(stat = 'count', aes(label = ..count..), hjust = 1.5, size = 3) +
    guides(fill = guide_legend(title = "Sample")) +
    scale_fill_manual(values = color_palette)

  return(p)
}

plot_list <- list()

for (i in seq_along(batches$Before)) {
  sce <- readRDS(batches$Before[i])
  sce1 <- as.SingleCellExperiment(readRDS(batches$After[i]))
  
  batch_name <- gsub(pattern = ".*/|\\.CellRanger.*", replacement = "", x = batches$Before[i])
  
  p1 <- plot_cellnum(data.frame(Sample = sce$Sample), paste0(batch_name, " Before QC"))
  p2 <- plot_cellnum(data.frame(Sample = sce1$Sample), paste0(batch_name, " After QC"))
  
  plot_list[[batch_name]] <- plot_grid(p1, p2, nrow = 2)
}

# Print the plots within markdown tabs
for (batch_name in names(plot_list)) {
  cat("###", batch_name, "\n")
  print(plot_list[[batch_name]])
  cat("\n")
}

G000231_batch1_Nasal_brushings

Breakdown of the samples.

G000231_batch2_Tonsils

Breakdown of the samples.

G000231_batch3_Adenoids

Breakdown of the samples.

G000231_batch4_Bronchial_brushings

Breakdown of the samples.

G000231_batch5_Nasal_brushings

Breakdown of the samples.

G000231_batch6_BAL

Breakdown of the samples.

G000231_batch7_Bronchial_brushings

Breakdown of the samples.

G000231_batch8_Adenoids

Breakdown of the samples.

G000231_batch9_Tonsils

Breakdown of the samples.

Library Size and detected genes before and after applying QC measures-

plot_qc_metrics <- function(sce, title) {
q1 <- plotColData(
    sce,
    "sum",
    x = "Sample",
    colour_by = "Sample",
    point_size = 0.5) +
    scale_y_log10() +
    annotation_logticks(
      sides = "l",
      short = unit(0.03, "cm"),
      mid = unit(0.06, "cm"),
      long = unit(0.09, "cm")) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0))

  q2 <- plotColData(
    sce,
    "detected",
    x = "Sample",
    colour_by = "Sample",
    point_size = 0.5) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0))

  qc_plots <- (q1 + q2) + plot_layout(guides = "collect", ncol = 2) +
    theme(legend.position = "none") 

  return(qc_plots)
}

plot_list <- list()

for (i in seq_along(batches$Before)) {
  sce_before <- readRDS(batches$Before[i])
  is.mito <- grepl(pattern = "^MT", rownames(sce_before))
  sce_before <- addPerCellQCMetrics(sce_before, subsets = list(mito = is.mito))
  sce_after <- as.SingleCellExperiment(readRDS(batches$After[i]))
  is.mito <- grepl(pattern = "^MT", rownames(sce_after))
  sce_after <- addPerCellQCMetrics(sce_after, subsets = list(mito = is.mito))
  
  batch_name <- gsub(pattern = ".*/|\\.CellRanger.*", replacement = "", x = batches$Before[i])
  
  # Generating QC plots and calculating medians
  q1 <- plot_qc_metrics(sce_before, paste0(batch_name, " Before QC"))
  q2 <- plot_qc_metrics(sce_after, paste0(batch_name, " After QC"))
  
  plot_list[[batch_name]] <- plot_grid(q1, q2, nrow = 2)
  
}

# Print the plots within markdown tabs
for (batch_name in names(plot_list)) {
  cat("###", batch_name, "\n")
  print(batch_name)
  print(plot_list[[batch_name]])
  cat("\n")
}