Validating Sequencing-based 10x Visium Identification of T Cell Population with Imaging-Based Spatial Transcriptomics
From last week’s results and selected cluster, I identified the genes LTB, CD247, and IL7R , all of which suggest a T cell population (or similar immune cell population comprising B cells). In working with the imaging-based dataset, I applied a similar clustering approach and used selected overexpressed markers to identify a similar cell type cluster. Though LTB was the most strongly correlated hit in the sequencing-based dataset, sparse detection of LTB in the imaging-based set made this an unconvincing marker gene for the selected cell type. I therefore opted to visualize IL7R and CD247, both of which were represented prominently in both the imaging and sequencing-based datasets and suggested a distinct niche in the tSNE-embedded gex space for the imaging-based dataset. Based on this identified location, I tuned the clustering parameters to best fit the intended cell type expression patterns for IL7R and CD247. I thus identified the illustrated cluster 5 as the comparable T cell population. I accordingly adjusted the code to accomodate the reduced gene set and data clustering in the imaging-based dataset. IL7R and CD247 are potent T cell markers. I also opted not to reduce the dataset to the top gene hits, in contrast to the sequencing-based dataset, and saw a significant improvement in clarity of embedding when using tSNE over PCA versus the same two methods used in the sequencing-based set.
5. Code (paste your code in between the ``` symbols)
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## SV Kammula | HW4
## HW4 GDV
## SV Kammula
data <- read.csv('pikachu.csv.gz')
library(ggplot2)
library(patchwork)
library(Rtsne)
library(ggrepel)
pos <- data[, 5:6]
rownames(pos) <- data$X
gexp <- data[, 7:ncol(data)]
norm <- gexp/rowSums(gexp) * 10000
rowSums(norm)
loggexp <- log10(gexp + 1)
com <- kmeans(loggexp, centers = 7)
clusters <- com$cluster
clusters <- as.factor(clusters)
names(clusters) <- rownames (gexp)
head(clusters)
pcs <- prcomp(loggexp)
df <- data.frame(pcs$x, clusters, gene = gexp[, 'CD4'])
ggplot(df, aes(x=PC1, y=PC2, col=clusters)) + geom_point()
ggplot(df, aes(x=PC1, y=PC2, col=gene)) + geom_point()
emb <- Rtsne::Rtsne(pcs$x[,1:10])$Y
head(emb)
pos$clusters <- clusters
ggplot(df, aes(x = emb[,1], y = emb[,2], col=clusters)) + geom_point()
df <- data.frame(emb, clusters, gene = loggexp[, 'IL7R'])
df$clusters_colored <- ifelse(df$clusters == 5, "Cluster 5", "Other")
pos$clusters_colored <- ifelse(pos$clusters == 5, "Cluster 5", "Other")
g1 <- ggplot(df, aes(x = emb[,1], y = emb[,2], col = clusters_colored)) +
geom_point(size = 0.75) +
scale_color_manual(values = c("Cluster 5" = "#8000FF", "Other" = "lightgray")) +
labs(color = "Cluster Assignment") +
theme_minimal() +
ggtitle('tSNE-embedded Capture Spots \nwith Cluster 5 Colored') +
xlab("tSNE1") +
ylab("tSNE2") +
theme(legend.position = "none")
g2 <- ggplot(pos, aes(x = aligned_x, y = aligned_y, col = clusters_colored)) +
geom_point(size = 0.5) +
scale_color_manual(values = c("Cluster 5" = "#8000FF", "Other" = "lightgray")) +
labs(color = "Cluster Assignment") +
ggtitle('Spatial Organization of Selected Cluster') +
theme_minimal() +
xlab("x position") +
ylab("y position")
g3 <- ggplot(df, aes(x = emb[,1], y = emb[,2], col = gene)) +
geom_point(size = 0.75) +
scale_color_gradient(high = "#8000FF", low = "lightgray") +
labs(color = "IL7R") +
theme_minimal() +
ggtitle('tSNE-embedded Capture Spots with \nSelected Cluster Colored') +
xlab("tSNE1") +
ylab("tSNE2")
df <- data.frame(emb, clusters, gene = loggexp[, 'LTB'])
g4 <- ggplot(df, aes(x = emb[,1], y = emb[,2], col = gene)) +
geom_point(size = 0.75) +
scale_color_gradient(high = "#8000FF", low = "lightgray") +
labs(color = "LTB") +
theme_minimal() +
ggtitle('tSNE-embedded Capture Spots with \nSelected Cluster Colored') +
xlab("tSNE1") +
ylab("tSNE2")
df <- data.frame(emb, clusters, gene = loggexp[, 'CXCR4'])
g5 <- ggplot(df, aes(x = emb[,1], y = emb[,2], col = gene)) +
geom_point(size = 0.75) +
scale_color_gradient(high = "#8000FF", low = "lightgray") +
labs(color = "CXCR4") +
theme_minimal() +
ggtitle('tSNE-embedded Capture Spots with \nSelected Cluster Colored') +
xlab("tSNE1") +
ylab("tSNE2")
df <- data.frame(emb, clusters, gene = loggexp[, 'CD247'])
g6 <- ggplot(df, aes(x = emb[,1], y = emb[,2], col = gene)) +
geom_point(size = 0.75) +
scale_color_gradient(high = "#8000FF", low = "lightgray") +
labs(color = "CD247") +
theme_minimal() +
ggtitle('tSNE-embedded Capture Spots with \nSelected Cluster Colored') +
xlab("tSNE1") +
ylab("tSNE2")
#df <- data.frame(emb, clusters, gene = loggexp[, 'PTPN22'])
#g5 <- ggplot(df, aes(x=emb[,1], y=emb[,2], col=gene)) + geom_point(size = .5)
## differential gene expression analysis
interest <- 5
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 == 5, i], gexp[clusters != 5, i])$p.val
})
# get log fold change
logfc <- sapply(colnames(gexp), function(i) {
log2(mean(norm[clusters == 5, i])/mean(norm[clusters != 5, i]))
})
valid_indices <- !is.na(pvalues)
filtered_pvalues = pvalues[valid_indices]
filtered_logfc = logfc[valid_indices]
# volcano plot
df_volc = data.frame(pvalues = -log10(filtered_pvalues), log_fc = filtered_logfc)
df_volc$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 <- -2
df_volc$color <- ifelse(df_volc$log_fc > upper_logfc, "Upregulated",
ifelse(df_volc$log_fc < lower_logfc, "Downregulated", "Other"))
g7 <- ggplot(df_volc, aes(x = log_fc, y = pvalues, color = color)) +
geom_point(size= 0.75) +
ggtitle("Differentially Expressed Genes in Cluster 5 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 + g6) / g7 + plot_annotation(tag_levels = 'A')
###