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differential_expression_blish_S100A8.md

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Differential expression analysis for selected cells in the COVID S100A8 regression

Jan Schleicher

library(Seurat)
library(dplyr)
library(reshape2)
library(ggplot2)
library(introdataviz)
library(future)
plan("multisession", workers = 4)
library(data.table)

Loading the data

For this analysis, we load the Seurat object containing the COVID-19 data from Wilk et al. 2021 and the extracted filter response from the S100A8 regression.

blish <- readRDS("../data/blish_seurat.rds")
filter_response <- read.csv("../output/filter_responses/blish_S100A8_filter_response.csv",
                            row.names = 1)

Identifying selected cells

We add selected cells, i.e., cells with a filter response that is larger than $0.3 \times (\text{maximum filter response})$.

threshold = .3
filter_response$selected_filter_0 <- filter_response$response_filter_0 > threshold *
  max(filter_response$response_filter_0)
cat("Number of selected cells for filter 0:", sum(filter_response$selected_filter_0))
## Number of selected cells for filter 0: 2945

blish <- AddMetaData(blish, filter_response)

DimPlot(blish, cells.highlight = WhichCells(blish, expression = selected_filter_0), reduction = "umap")

coarse_cell_type_selected_0 <- paste(blish@meta.data$cell.type.coarse, blish@meta.data$selected_filter_0)
names(coarse_cell_type_selected_0) <- row.names(blish@meta.data)
blish <- AddMetaData(blish, coarse_cell_type_selected_0, col.name = "coarse_cell_type_selected_0")

Analysing differential expression between selected and unselected cells

For a detailed characterization of selected cells, we determined differentially expressed genes between selected and unselected cells of the same type. We did this analysis for the most highly enriched selected cell types.

extract_data_for_violin <- function(adata, genes, cell_type_field, cell_types) {
  selected_cells <- row.names(adata@meta.data[apply(adata[[cell_type_field]], 1, function(x) x %in% cell_types),])
  data <- FetchData(adata, vars = c(genes, cell_type_field), cells = selected_cells, slot = "data")
  return(reshape2::melt(data, id.vars = cell_type_field, variable.name = "gene", value.name = "expression"))
}

make_violin_plot <- function(adata, genes, cell_type_field, cell_types, title, base_size=12, palette="Set2") {
  p <- ggplot(extract_data_for_violin(adata, genes, cell_type_field, cell_types),
              aes(x = gene, y = expression, fill = get(cell_type_field))) +
    geom_split_violin(scale = "width",
                      trim = T) +
    scale_fill_brewer(name = "selected", labels = c("false", "true"), palette = palette) +
    theme_classic(base_size = base_size) +
    theme(plot.title = element_text(hjust = 0.5),
          axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title=title)
  return(p)
}
filt <- "coarse_cell_type_selected_0"
cell_types <- c("Developing neutrophil", "PB")

cat("Identifying markers for", filt, "\n")
## Identifying markers for coarse_cell_type_selected_0

Idents(blish) <- filt
for (ct in cell_types) {
  print(ct)
  cat("...identifying markers for selected", ct, "cells \n")
  markers <- FindMarkers(blish, ident.1 = paste(ct, "TRUE", sep = " "),
                         ident.2 = paste(ct, "FALSE", sep = " "))
  markers <- markers[markers["p_val_adj"] < 0.05,]
  markers <- cbind(gene = row.names(markers), markers)
  fwrite(markers, file = paste("../output/tables/blish_S100A8", filt,
                               gsub(",*\\s|/", "_", ct), "deg.csv",
                               sep = "_"))
  p <- make_violin_plot(blish,
                        row.names(head(markers[-grep('^MT-', rownames(markers)),], n = 6)),
                        filt, c(paste(ct, "TRUE", sep = " "), paste(ct, "FALSE", sep = " ")),
                        ct, base_size = 14, palette = "Paired")
  print(p)
  svg(paste("../output/figures/blish_S100A8/blish_S100A8", filt,
               gsub(",*\\s|/", "_", ct), "deg.svg",  sep = "_"), width = 4, height = 4)
  print(p)
  dev.off()
}
## [1] "Developing neutrophil"
## ...identifying markers for selected Developing neutrophil cells


## [1] "PB"
## ...identifying markers for selected PB cells