HW3: Exploring Cell Type with Differentially upregulated CD52
1. Figure Description.
Figure A: Cluster 1 is highlighted in orange in PCA space, while the remaining six clusters are shown in grey. The axes represent PC1 and PC2. Figure B: Cluster 1 is highlighted in orange to show its distribution in physical space, with the remaining six clusters in grey. The axes represent x and y spot positions. Figure C: A volcano plot comparing gene expression in Cluster 1 versus the other clusters. The y-axis represents p-values in -log10 scale, while the x-axis represents log2 fold changes between Cluster 1 and the rest of the clusters. Figure D: CD52 gene expression is visualized in PCA space, highlighted in a gradient red. Figure E: CD52 gene expression is visualized in physical space, highlighted in a gradient red. Figure G: CD4 gene expression is visualized in PCA space, highlighted in a gradient red.
2. Cluster interpretation.
Cluster 1 spots show differentially upregulated CD52. We know this because, in Figures A and D, Cluster 1 has the highest CD52 gene expression level in PCA space. This is also evident in Figures B and E, where spots in Cluster 1 exhibit a high expression level of CD52 in physical space. The cell marker CD52 is commonly found in mature lymphocytes, natural killer (NK) cells, eosinophils, neutrophils, monocytes/macrophages, and dendritic cells (DCs). Therefore, Cluster 1 likely belongs to one of these populations. Additionally, CD4, a marker for helper T cells, is also differentially upregulated in Cluster 1. Combining these two markers, it is reasonable to assume that Cluster 1 represents helper T cells.
3. Citation.
https://pubmed.ncbi.nlm.nih.gov/28283679/#:~:text=Introduction%3A%20CD52%20(Campath%2D1,and%20dendritic%20cells%20(DCs).
https://clinicalinfo.hiv.gov/en/glossary/cd4-t-lymphocyte#:~:text=A%20type%20of%20lymphocyte.,cells)%2C%20to%20fight%20infection.
4. Code
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library(ggplot2)
library(patchwork)
file <- '"~/Downloads/eevee.csv.gz"'
data <- read.csv(file)
data[1:5,1:10]
pos <- data[, 3:4]
rownames(pos) <- data$cell_id
gexp <- data[, 5:ncol(data)]
rownames(gexp) <- data$barcode
head(gexp)
head(pos)
gexp[1:5, 1:10]
dim(gexp)
# limiting to top 1000 most highly expressed genes
topgenes <- names(sort(colSums(gexp), decreasing=TRUE)[1:2000])
gexpsub <- gexp[,topgenes]
gexpsub[1:5,1:5]
dim(gexpsub)
# normalization
norm <- gexpsub/rowSums(gexpsub) *10000
loggexp <- log10(norm+1)
dim(loggexp)
## kmeans
ks = 1:25
totw <- sapply(ks, function(k) {
print(k)
com <- kmeans(loggexp, centers=k)
return(com$tot.withinss)
})
g1 <- plot(ks, totw)
# pick k = 7
com <- kmeans(loggexp, centers=7)
clusters <- com$cluster
clusters <- as.factor(clusters)
names(clusters) <- rownames(loggexp)
head(clusters)
# PCA
pcs <- prcomp(loggexp)
df <- data.frame(pcs$x,clusters)
ggplot(df, aes(x=PC1, y=PC2, col= clusters)) + geom_point()
g3<- ggplot(df, aes(x=PC1, y=PC2, col= factor(clusters==6, labels = c("Other Clusters","Cluster 1")))) +
geom_point() +
scale_color_manual(values = c("Cluster 1" = "orange", "Other Clusters" = "grey"))+
labs(title = "A: Focused Cluster 1 on PCA Space", col = "Cluster") +
theme_minimal()
df <- data.frame(pos,loggexp,clusters)
g4 <- ggplot(df, aes(x = aligned_x, y = aligned_y, col = factor(clusters==6, labels = c("Other Clusters","Cluster 1")))) +
geom_point() +
scale_color_manual(values = c("Cluster 1" = "orange", "Other Clusters" = "grey"))+
labs(title = "B: Focused Cluster 1 on Physical Space", col = "Clusters",
x = "Cell Position for x",
y = "Cell Position for y") +
theme_minimal()
g3 + g4
# differential expression
cellsOfInterest <- names(clusters)[clusters == 6]
otherCells <- names(clusters)[clusters != 6]
results <- sapply(1:ncol(gexpsub), function(i) {
genetest <- gexpsub[,i]
names(genetest) <- rownames(gexpsub)
out <- t.test(genetest[cellsOfInterest], genetest[otherCells], alternative = 'greater')
out$p.value
})
names(results) <- colnames(gexpsub)
results <- sort(results, decreasing = FALSE)
length(results)
Cluster1Cell <- gexpsub[cellsOfInterest,]
SumCluster1Cell <- colSums(Cluster1Cell)/length(cellsOfInterest)
OtherClustersCell <- gexpsub[otherCells,]
SumOtherClustersCell <- colSums(OtherClustersCell)/length(otherCells)
log_results <- results
log_2FC <- log2(SumCluster1Cell/SumOtherClustersCell)
ggplot(data.frame(log_results), aes(x = log_results))+
geom_histogram(binwidth = 1, fill = "lightblue", color = "black", alpha = 0.7)+
labs(title = "C: Histogram of -log10 Transformed p-values",
x = "-log10(p-value)",
y = "Frequency") +
theme_minimal()
df <- data.frame(log_results, log_2FC)
df$diffexpressed <- "NO"
df$diffexpressed[df$log_2FC > 0.6 & df$log_results < (0.05)] <- "UP"
df$diffexpressed[df$log_2FC < -0.6 & df$log_results < (0.05)] <- "DOWN"
g5<- ggplot(df, aes(y=-log10(log_results), x = log_2FC, col = diffexpressed)) + geom_point() +
geom_vline(xintercept = c(-0.6, 0.6), col = "gray", linetype = 'dashed') +
geom_hline(yintercept = -log10(0.05), col = "gray", linetype = 'dashed') +
labs(title = "C: Volcano Graph",
y = "-log10(p-value)",
x = "Log2FC", col = "Change in Expression") +
scale_color_manual(values = c("lightblue", "grey", "pink"),
labels = c("Downregulated", "Not significant", "Upregulated"))+
theme_minimal()
# one gene in PCA
df <- data.frame(pcs$x, clusters, gene = loggexp[, 'CD52'])
g6 <- ggplot(df, aes(x=PC1, y=PC2, col=gene)) + labs(title = "D: CD52 expression on PCA Space", col = "CD52") +
scale_color_gradient(low = 'lightgrey', high = 'red') + geom_point() + theme_minimal()
# one gene in physical space
df <- data.frame(pos, gene = loggexp[,"CD52"])
g7 <- ggplot(df, aes(x = aligned_x, y = aligned_y, col = gene)) +
labs(title = "E: CD52 expression on Physical Space ",
x = "Cell Position for x",
y = "Cell Position for y",
col = "CD52") + scale_color_gradient(low = 'lightgrey', high = 'red') +
geom_point() + theme_minimal()
g3 + g4 + g5 + g6 + g7