CODEX dataset analysis
We conducted normalization, standardization, dimensionality reduction, k-means clustering, and differential expression analysis to reveal two distinct cell clusters within the CODEX data. tSNE plots and marker expression heatmaps is plotted with their spatial organization, indicating structural significance of the clustering. Cluster 1 expressed high levels of T cell markers (CD3e and CD45RO), as well as CD21, which suggest memory T cells characteristic. Cluster 4 was characterized by strong expression of B cell markers CD20 and CD21. Memory T cells are primarily located in the periarteriolar lymphoid sheaths and B cells in the lymphoid follicles, we concluded that the tissue of interest in part of the white pulp region [1, 2].
[1] Mebius, R. E., & Kraal, G. (2005). Structure and function of the spleen. Nature reviews immunology, 5(8), 606-616. [2] Waterfield, J. D., Ekstedt, R. D., & Möller, G. (1977). Functional Heterogeneity of Splenic T Lymphocyte Subpopulations I. Determination of Splenic Subpopulations by the Use of Mitogenic Probes. Scandinavian Journal of Immunology, 6(6‐7), 615-623.
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library(ggplot2)
library(ggpubr)
library(dplyr)
library(cluster)
library(scales)
library(Rtsne)
library(factoextra)
library(tidyverse)
file <- "/Users/sky2333/Downloads/genomic-data-visualization-2025/data/codex_spleen_3.csv"
data <- read.csv(file)
gene_expr <- data[, 4:ncol(data)]
sample_names <- rownames(gene_expr)
gene_names <- colnames(gene_expr)
gene_expr <- t(apply(log1p(gene_expr / rowSums(gene_expr) * 1e6), 1, scale))
rownames(gene_expr) <- sample_names
colnames(gene_expr) <- gene_names
set.seed(222)
elbow_plot <- fviz_nbclust(gene_expr, kmeans, method = "wss",k.max = 10,nstart = 25,iter.max = 25) +
geom_vline(xintercept = 5, linetype = "dashed", color = "red") +
labs(title = "Optimal Number of Clusters (Elbow Method)")
#pca_result <- prcomp(gene_expr, center = TRUE, scale. = TRUE)
pca_result <- Rtsne(gene_expr, perplexity = 30, theta = 0.5, dims = 2, pca = TRUE, verbose = TRUE)
data$PC1 <- pca_result$Y[, 1]
data$PC2 <- pca_result$Y[, 2]
k <- 5
kmean_result <- kmeans(gene_expr, centers = k, nstart = 25, iter.max = 1000)
pca_plot <- ggplot(data, aes(x = PC1, y = PC2, color = Cluster)) +
geom_point(alpha = 0.5, size = 0.5) +
scale_color_manual(values = hue_pal()(k)) +
labs(title = "tSNE of Gene Expression with K-Means Clusters", x = "PC1", y = "PC2", color = "Cluster") +
theme_minimal()
spatial_plot <- ggplot(data, aes(x = x, y = y, color = Cluster)) +
geom_point(alpha = 0.5, size = 0.5) +
scale_color_manual(values = hue_pal()(k)) +
labs(title = "Spatial Map Colored by Gene Expression Clusters", x = "X Coordinate", y = "Y Coordinate", color = "Cluster") +
theme_minimal()
spatial_plot
pca_plot
cluster_interest <- "4"
genes_pvals <- sapply(gene_names, function(gene) {
wilcox.test(
gene_expr[data$Cluster == cluster_interest, gene],
gene_expr[data$Cluster != cluster_interest, gene]
)$p.value
})
genes_pvals_adj <- p.adjust(genes_pvals, method = "fdr")
logFC <- apply(gene_expr, 2, function(gene) {
mean(gene[data$Cluster == cluster_interest]) - mean(gene[data$Cluster != cluster_interest])
})
de_genes <- data.frame(Gene = gene_names, p_value = genes_pvals, adj_p_value = genes_pvals_adj, logFC = logFC)
volcano_plot_1 <- ggplot(de_genes, aes(x = logFC, y = -log10(adj_p_value))) +
geom_point(alpha = 0.5) +
geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "red") +
geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "blue") +
theme_minimal() +
labs(title = paste("Volcano Plot of DE Genes of cluster ", cluster_interest), x = "Log FC", y = "-log Adjusted p-val")
top_genes <- arrange(filter(de_genes, adj_p_value < 0.05 & abs(logFC) > 1), adj_p_value)
selected_gene <- top_genes$Gene[1]
top_genes
pca_gene_plot_1 <- ggplot(data, aes(x = PC1, y = PC2, color = gene_expr[, selected_gene])) +
geom_point(alpha = 0.5, size = 0.5) +
scale_color_gradientn(
colors = c("blue", "cyan", "green", "yellow", "red"),
limits = range(gene_expr[, selected_gene]),
breaks = pretty(gene_expr[, selected_gene], n = 5)
) +
labs(title = paste("tSNE of expression of", selected_gene), x = "PC1", y = "PC2", color = "Expression") +
theme_minimal()
spatial_gene_plot_1 <- ggplot(data, aes(x = x, y = y, color = gene_expr[, selected_gene])) +
geom_point(alpha = 0.5, size = 0.5) +
scale_color_gradientn(
colors = c("blue", "cyan", "green", "yellow", "red"),
limits = range(gene_expr[, selected_gene]),
breaks = pretty(gene_expr[, selected_gene], n = 5)
) +
labs(title = paste("Spatial Map of expression of", selected_gene), x = "X Coordinate", y = "Y Coordinate", color = "Expression") +
theme_minimal()
volcano_plot
spatial_gene_plot
pca_gene_plot
cluster_interest <- "1"
genes_pvals <- sapply(gene_names, function(gene) {
wilcox.test(
gene_expr[data$Cluster == cluster_interest, gene],
gene_expr[data$Cluster != cluster_interest, gene]
)$p.value
})
genes_pvals_adj <- p.adjust(genes_pvals, method = "fdr")
logFC <- apply(gene_expr, 2, function(gene) {
mean(gene[data$Cluster == cluster_interest]) - mean(gene[data$Cluster != cluster_interest])
})
de_genes <- data.frame(Gene = gene_names, p_value = genes_pvals, adj_p_value = genes_pvals_adj, logFC = logFC)
volcano_plot_2 <- ggplot(de_genes, aes(x = logFC, y = -log10(adj_p_value))) +
geom_point(alpha = 0.5) +
geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "red") +
geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "blue") +
theme_minimal() +
labs(title = paste("Volcano Plot of DE Genes of cluster ", cluster_interest), x = "Log FC", y = "-log Adjusted p-val")
top_genes <- arrange(filter(de_genes, adj_p_value < 0.05 & abs(logFC) > 1), adj_p_value)
selected_gene <- top_genes$Gene[1]
top_genes
pca_gene_plot_2 <- ggplot(data, aes(x = PC1, y = PC2, color = gene_expr[, selected_gene])) +
geom_point(alpha = 0.5, size = 0.5) +
scale_color_gradientn(
colors = c("blue", "cyan", "green", "yellow", "red"),
limits = range(gene_expr[, selected_gene]),
breaks = pretty(gene_expr[, selected_gene], n = 5)
) +
labs(title = paste("tSNE of expression of", selected_gene), x = "PC1", y = "PC2", color = "Expression") +
theme_minimal()
spatial_gene_plot_2 <- ggplot(data, aes(x = x, y = y, color = gene_expr[, selected_gene])) +
geom_point(alpha = 0.5, size = 0.5) +
scale_color_gradientn(
colors = c("blue", "cyan", "green", "yellow", "red"),
limits = range(gene_expr[, selected_gene]),
breaks = pretty(gene_expr[, selected_gene], n = 5)
) +
labs(title = paste("Spatial Map of expression of", selected_gene), x = "X Coordinate", y = "Y Coordinate", color = "Expression") +
theme_minimal()
volcano_plot
spatial_gene_plot
pca_gene_plot
final_plot <- ggarrange(
elbow_plot, pca_plot, spatial_plot, volcano_plot_1, volcano_plot_2, pca_gene_plot_1, spatial_gene_plot_1, pca_gene_plot_2, spatial_gene_plot_2,
ncol = 3, nrow = 3,
width = 40, height = 40
)
options(repr.plot.width = 15, repr.plot.height = 15)
final_plot