Interrogating Spatial Spot Cluster Differential Gene Expression with 10x Visium
In these panels, I am depicting the representation of a 10x visium dataset in latent tSNE-embedded space and over the original spatial slide coordinates. I select a cluster based on the tSNE-embedded space (A) and visualize the spatial organization of the same cluster of spots on the spatial array (B). I then use the Wilcox test and compute fold change for each gene in the selected cluster 3 with respect to the same gene expression levels across the rest of the spot gene expression data. This differential gene expression is expressed as a function of magnitude of differential (fold change) and significance of the differential (p-value) and is shown illustrated in a volcano plot, with significant hits colored on the extreme right and left of the plot (C). From this plot, I select the gene LTB, and visualize corresponding normalized expression of LTB across the tSNE-embedded space and over spatial coordinates. LTB (lymphotoxin beta) encodes a protein that plays a crucial role in the immune system and inflammation, and as the significance of this hit would suggest, it is heavily correlated with cluster 3 phenotype. I conclude that the selected cluster 3 is likely an immune-related cell type– either T cells, B cells, or dendritic cells. Functionally, LTB anchors lymphotoxin-alpha to the surface of cells to mediate lymphotoxic activity. Further interrogation yields a similar expression pattern of CD4, reinforcing this conclusion.
1.https://www.ncbi.nlm.nih.gov/gene/4050#:~:text=Lymphotoxin%20beta%20is%20a%20type,Long%20Non%2DCoding%20RNA%20AL928768.
- https://www.uniprot.org/uniprotkb/Q06643/entry
- https://www.proteinatlas.org/ENSG00000227507-LTB
5. Code (paste your code in between the ``` symbols)
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## SV Kammula | HW4
data <- read.csv('eevee.csv.gz')
library(ggplot2)
library(patchwork)
library(Rtsne)
library(ggrepel)
pos <- data[, 3:4]
rownames(pos) <- data$X
gexp <- data[, 5:ncol(data)]
norm <- gexp/rowSums(gexp) * 10000
rowSums(norm)
topgenes <- names(sort(colSums(norm), decreasing=TRUE)[1:1000])
normsub <- norm[,topgenes]
loggexp <- log10(gexp + 1)
com <- kmeans(loggexp, centers = 6)
clusters <- com$cluster
clusters <- as.factor(clusters)
names(clusters) <- rownames (gexp)
head(clusters)
pcs <- prcomp(loggexp)
df <- data.frame(pcs$x, clusters, gene = gexp[, 'CD4'])
ggplot(df, aes(x=PC1, y=PC2, col=clusters)) + geom_point()
## embedding
emb <- Rtsne::Rtsne(pcs$x[,1:10])$Y
head(emb)
df <- data.frame(emb, clusters, gene = loggexp[, 'LTB'])
ggplot(df, aes(x=X1, y=X2, col=clusters)) + geom_point()
# choose cluster 3; look at CD4
df$clusters_colored <- ifelse(df$clusters == 3, "Cluster 3", "Other")
# panel 1
g1 <- ggplot(df, aes(x = X1, y = X2, col = clusters_colored)) +
geom_point() +
scale_color_manual(values = c("Cluster 3" = "#8000FF", "Other" = "lightgray")) +
labs(color = "Cluster Assignment") +
theme_minimal() +
ggtitle('tSNE-embedded Capture Spots with \nSelected Cluster Colored') +
xlab("tSNE1") +
ylab("tSNE2") +
theme(legend.position = "none")
# panel 2
g2 <- ggplot(pos, aes(x = aligned_x, y = aligned_y, col = df$clusters_colored)) +
geom_point() +
scale_color_manual(values = c("Cluster 3" = "#8000FF", "Other" = "lightgray")) +
labs(color = "Cluster Assignment") +
ggtitle('Spatial Organization of Selected Cluster') +
theme_minimal() +
xlab("x position") +
ylab("y position")
# panel 3
g4 <- ggplot(df, aes(x = X1, y = X2, col = gene)) +
geom_point() +
scale_color_gradient(high = "#8000FF", low = "lightgray") +
labs(color = 'LTB') +
theme_minimal() +
ggtitle('tSNE-embedded LTB gene expression') +
xlab("tSNE1") +
ylab("tSNE2") +
theme(legend.position = "none")
# panel 4
g5 <- ggplot(pos, aes(x = aligned_x, y = aligned_y, col = df$gene)) +
geom_point() +
scale_color_gradient(high = "#8000FF", low = "lightgray") +
labs(color = 'LTB') +
ggtitle('Spatial Organization of LTB gene expression') +
theme_minimal() +
xlab("x position") +
ylab("y position")
interest <- 3
cellsOfInterest <- names(clusters)[clusters == interest]
otherCells <- names(clusters)[clusters != interest]
i <- 'CD4' #find on expressed in cluster of interest
genetest <- norm[,i]
names(genetest) <- rownames(norm)
genetest[cellsOfInterest]
genetest[otherCells]
t.test(genetest[cellsOfInterest], genetest[otherCells], )
# differential gene expression
pvalues <- sapply(colnames(norm), function(i) {
wilcox.test(gexp[clusters == 3, i], gexp[clusters != 3, i])$p.val
})
# get log fold change
logfc <- sapply(colnames(gexp), function(i) {
log2(mean(norm[clusters == 3, i])/mean(norm[clusters != 3, i]))
})
valid_indices <- !is.na(pvalues)
filtered_pvalues = pvalues[valid_indices]
filtered_logfc = logfc[valid_indices]
# volcano plot
df_volc = data.frame(pvalues = -log10(filtered_pvalues), log_fc = filtered_logfc)
df_volc$genes <- rownames(df_volc)
#df_volc_cleaned <- df_volc[!is.nan(df_volc$p_values) & !is.nan(df_volc$logFC), ]
upper_logfc <- 2
lower_logfc <- -3
df_volc$color <- ifelse(df_volc$log_fc > upper_logfc, "Upregulated",
ifelse(df_volc$log_fc < lower_logfc, "Downregulated", "Other"))
g3 <- ggplot(df_volc, aes(x = log_fc, y = pvalues, color = color)) +
geom_point(size= 0.75) +
ggtitle("Differentially Expressed Genes in Cluster 3 Compared to Others") +
scale_color_manual(values = c("Upregulated" = "#8000FF",
"Downregulated" = "#FF8000",
"Other" = "lightgray")) +
labs(color = "Significant DE Gene")
#scale_color_manual(values = c("Cluster 3" = "#8000FF", "Other" = "lightgray")) +
xlab("log2(FC)") +
ylab("-log10(p-value)")
(g1 + g2) / g3 / (g4 + g5) + plot_annotation(tag_levels = 'A')
###