preparing gene expression matrix: calculate DESeq2 results

#Input: merged_gene_counts.txt

setwd("/home/jhuang/DATA/Data_Susanne_Carotis_RNASeq/run_2023_GSVA/")

library("AnnotationDbi")
library("clusterProfiler")
library("ReactomePA")
#BiocManager::install("org.Hs.eg.db")
library("org.Hs.eg.db")
library(DESeq2)
library(gplots)

d.raw<- read.delim2("merged_gene_counts.txt",sep="\t", header=TRUE, row.names=1)

colnames(d.raw)<-c("gene_name", "leer_mock_2h_r2", "Ace2_mock_2h_r2", "leer_inf_24h_r1", "Ace2_inf_2h_r1", "leer_inf_24h_r2", "leer_inf_2h_r1", "leer_mock_2h_r1", "leer_inf_2h_r2", "Ace2_inf_2h_r2", "Ace2_mock_2h_r1", "Ace2_inf_24h_r2", "Ace2_inf_24h_r1")

col_order <- c("gene_name", "leer_mock_2h_r1","leer_mock_2h_r2","leer_inf_2h_r1","leer_inf_2h_r2","leer_inf_24h_r1","leer_inf_24h_r2","Ace2_mock_2h_r1","Ace2_mock_2h_r2","Ace2_inf_2h_r1","Ace2_inf_2h_r2","Ace2_inf_24h_r1","Ace2_inf_24h_r2")

reordered.raw <- d.raw[,col_order]
reordered.raw$gene_name <- NULL
#d <- d.raw[rowSums(reordered.raw>3)>2,]

condition = as.factor(c("leer_mock_2h","leer_mock_2h","leer_inf_2h","leer_inf_2h","leer_inf_24h","leer_inf_24h","Ace2_mock_2h","Ace2_mock_2h","Ace2_inf_2h","Ace2_inf_2h","Ace2_inf_24h","Ace2_inf_24h"))
ids = as.factor(c("leer_mock_2h_r1","leer_mock_2h_r2","leer_inf_2h_r1","leer_inf_2h_r2","leer_inf_24h_r1","leer_inf_24h_r2","Ace2_mock_2h_r1","Ace2_mock_2h_r2","Ace2_inf_2h_r1","Ace2_inf_2h_r2","Ace2_inf_24h_r1","Ace2_inf_24h_r2"))

#cData = data.frame(row.names=colnames(reordered.raw), condition=condition,  batch=batch, ids=ids)
#dds<-DESeqDataSetFromMatrix(countData=reordered.raw, colData=cData, design=~batch+condition)
cData = data.frame(row.names=colnames(reordered.raw), condition=condition, ids=ids)
dds<-DESeqDataSetFromMatrix(countData=reordered.raw, colData=cData, design=~condition)

#----more detailed and specific with the following code!----
dds$condition <- relevel(dds$condition, "Ace2_mock_2h")
dds = DESeq(dds, betaPrior=FALSE)  # betaPrior default value is FALSE
resultsNames(dds)

preparing selected_geneSets in gsva(exprs, selected_geneSets, method=”gsva”). Note that methods are different than methods for nanoString, here are ENSEMBL listed.

#Input: "Signatures.xls" + "Signatures_additional.xls"
library(readxl)
library(gridExtra)
library(ggplot2)
library(GSVA)
# Paths to the Excel files
file_paths <- list("Signatures.xls", "Signatures_additional.xls")
# Get sheet names for each file
sheet_names_list <- lapply(file_paths, excel_sheets)

# Initialize an empty list to hold gene sets
geneSets <- list()
# Loop over each file path and its corresponding sheet names
for (i in 1:length(file_paths)) {
  file_path <- file_paths[[i]]
  sheet_names <- sheet_names_list[[i]]
  # Loop over each sheet, extract the ENSEMBL IDs, and add to the list
  for (sheet in sheet_names) {
    # Read the sheet
    data <- read_excel(file_path, sheet = sheet)

    # Process the GeneSet names (replacing spaces with underscores, for example)
    gene_set_name <- gsub(" ", "_", unique(data$GeneSet)[1])

    # Add ENSEMBL IDs to the list
    geneSets[[gene_set_name]] <- as.character(data$ENSEMBL)
  }
}

# Print the result to check
print(geneSets)
summary(geneSets)
#desired_geneSets <- c("Monocytes", "Plasma_cells", "T_regs", "Cyt._act._T_cells", "Neutrophils", "Inflammatory_neutrophils", "Suppressive_neutrophils", "LDG", "CD40_activated")
desired_geneSets <- c("IFN", "TNF", "IL-6R_complex", "IL-1_cytokines", "Pro-inflam._IL-1", "Monocyte_secreted", "Apoptosis", "NFkB_complex",   "NLRP3_inflammasome")
selected_geneSets <- geneSets[desired_geneSets]
# Print the selected gene sets
print(selected_geneSets)

prepare violin plots

# 0. for Nanostring, the GSVA input requires a gene expression matrix 'exprs' where rows are genes and columns are samples. This matrix must be in non-log space.
#exprs <- exprs(filtered_or_neg_target_m666Data)
# 0. for RNAseq, the GSVA input requires a gene expression matrix where rows are genes and columns are samples. This matrix must be in non-log space.
exprs <- counts(dds, normalized=TRUE)

# 1. Compute GSVA scores:
gsva_scores <- gsva(exprs, selected_geneSets, method="gsva")

# 2. Convert to data.frame for ggplot:
gsva_df <- as.data.frame(t(gsva_scores))

# 3. Add conditions to gsva_df:
gsva_df$Condition <- dds$condition

# 4. Filter the gsva_df to retain only the desired conditions:
#group 1 vs. group 3 in the nanostring data
gsva_df_filtered <- gsva_df[gsva_df$Condition %in% c("Ace2_mock_2h", "Ace2_inf_24h"), ]

# 5. Define a function to plot violin plots:
# Update the condition levels in gsva_df_filtered to ensure the desired order on x-axis:
gsva_df_filtered$Condition <- gsub("Ace2_mock_2h", "Group3", gsva_df_filtered$Condition)  #group3=mock
gsva_df_filtered$Condition <- gsub("Ace2_inf_24h", "Group1a", gsva_df_filtered$Condition)  #group1a=infection
gsva_df_filtered$Condition <- factor(gsva_df_filtered$Condition, levels = c("Group1a", "Group3"))

plot_violin <- function(data, gene_name) {
  # Calculate the t-test p-value for the two conditions
  condition1_data <- data[data$Condition == "Group1a", gene_name]
  condition2_data <- data[data$Condition == "Group3", gene_name]
  p_value <- t.test(condition1_data, condition2_data)$p.value
  # Convert p-value to annotation
  p_annotation <- ifelse(p_value < 0.01, "**", ifelse(p_value < 0.05, "*", ""))
  rounded_p_value <- paste0("p = ", round(p_value, 2))
  plot_title <- gsub("_", " ", gene_name)
  p <- ggplot(data, aes(x=Condition, y=!!sym(gene_name), fill=Condition)) +
    geom_violin(linewidth=1.2) + 
    scale_fill_manual(values = custom_colors) +
    labs(title=plot_title, y="GSVA Score") +
    ylim(-1, 1) +
    theme_light() +
    theme(
      axis.title.x = element_text(size=12),
      axis.title.y = element_text(size=12),
      axis.text.x  = element_text(size=10),
      axis.text.y  = element_text(size=10),
      plot.title   = element_text(size=12, hjust=0.5),
      legend.position = "none" # Hide legend since the colors are self-explanatory
    )
  # Add p-value annotation to the plot
  p <- p + annotate("text", x=1.5, y=0.9, label=paste0(p_annotation, " ", rounded_p_value), size=5, hjust=0.5)
  return(p)
}
# 6. Generate the list of plots in a predefined order:
genes <- colnames(gsva_df_filtered)[!colnames(gsva_df_filtered) %in% "Condition"]
genes <- genes[match(desired_order, genes)]
genes <- genes[!is.na(genes)]
first_row_plots <- lapply(genes, function(gene) plot_violin(gsva_df_filtered, gene))

generating two 3×3 grid plots

# Start by extracting the 1-9 plots from each list
first_row_plots <- first_row_plots[1:9]

# Function to modify the individual plots based on position in the grid
modify_plot <- function(p, row, col) {
  p <- p + theme(axis.title.y = element_blank()) # remove y-axis title if not the first plot
  p <- p + theme(
    axis.title.x = element_blank(), # remove x-axis title for all plots
    axis.title.y = element_blank(), # remove x-axis title for all plots
    axis.text = element_text(size = 14), # Increase axis text size
    axis.title = element_text(size = 16), # Increase axis title size
    plot.title = element_text(size = 16) # Increase plot title size
  )
  return(p)
}
# Apply the modifications to the plots
for (i in 1:9) {
  first_row_plots[[i]] <- modify_plot(first_row_plots[[i]], 1, i)
}
# Increase the font size of x-axis labels for each plot
for (i in 1:9) {
  first_row_plots[[i]] <- first_row_plots[[i]] + theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 12))
}
# Pad first_row_plots to have 9 plots
remaining_plots <- 9 - length(first_row_plots)
if (remaining_plots > 0) {
  first_row_plots <- c(first_row_plots, rep(list(NULL), remaining_plots))
}

# Convert the first_row_plots to a matrix and draw
plots_matrix_1 <- matrix(first_row_plots, ncol=3, byrow=TRUE)
png("Carotis_RNA-seq_grid_1.png", width=600, height=600)
do.call("grid.arrange", c(plots_matrix_1, list(ncol=3)))
dev.off()