HW5: Identifying Cell Types and Tissue Structures in CODEX data
1. Figure Description.
Figure A: 6 clusters in physical space. The axes represent x and y position. Figure B: 6 clusters in t-SNE space. The axes represent X1 and X2. Figure C: CD4 expression on physical space. The axes represent x and y position. Figure D: CD2 expression on t-SNE space. The axes represent X1 and X2. Figure E: CD21 expression on physical space. The axes represent x and y position. Figure F: CD21 expression on t-SNE space. The axes represent X1 and X2.
2. Tissue Structure Identification.
I believe the tissue presented in the CODEX dataset is white pulp. Observing the physical space, the K-means clustering reveals a clear pattern: Cluster 4 forms two dense cell regions, while Cluster 1 surrounds Cluster 4.
To further investigate, I performed differential gene expression analysis. The results for Cluster 4 show that many upregulated genes (CD21, CD20, CD1c, CD35, HLA-DR, CD44, CD11c, CD34) are primarily expressed in B cells. Additionally, in the spatial layout, CD21 is highlighted in blue around the Cluster 1 region. Based on these findings, Cluster 4 is likely composed of B cells.
For Cluster 1, which forms a surrounding layer around Cluster 4, differential gene expression analysis reveals upregulated markers such as SMActin, CD4, CD45, PanCK, CD3e, CD45RO, CD35, CD20, CD44, CD5, HLA-DR, Podoplanin, Collagen IV, CD21, CD11c, ECAD, and CD1c. Some of these markers are associated with B cells, while others correspond to T cells and epithelial cells. Given the close proximity of Clusters 1 and 4, it is possible that some B cell markers from Cluster 4 are also detected in Cluster 1. However, Cluster 1 exhibits strong T cell markers, suggesting that it is primarily composed of T cells
Based on prior research, white pulp in the spleen consists of:
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1. A germinal center, primarily made up of B cells.
2. Periarteriolar lymphoid sheaths (PALS), which are rich in T cells.
This aligns well with the clustering results:
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• Cluster 4 forms a dense center of B cells.
• Cluster 1, composed of T cells and some B cells, surrounds this center.
Thus, this tissue should be white pulp.
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. https://www.sciencedirect.com/science/article/abs/pii/S1567576900000461#:~:text=The%20Complement%20Receptor%20Type%202,of%20the%20C3%20complement%20protein. https://medlineplus.gov/lab-tests/cd4-lymphocyte-count/ https://pmc.ncbi.nlm.nih.gov/articles/PMC1828535/
4. Code
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library(ggplot2)
library(patchwork)
file <- "~/Desktop/data visualization/codex_spleen_3.csv.gz"
data <- read.csv(file)
data[1:5,1:10]
dim(data)
pos <- data[, 2:3]
rownames(pos) <- data$X
gexp <- data[, 5:ncol(data)]
rownames(gexp) <- data$X
head(gexp)
head(pos)
# normalization
norm <- gexp/log10(data$area+1) * 3
norm[1:5,1:5]
## kmeans
ks = 1:25
totw <- sapply(ks, function(k) {
print(k)
com <- kmeans(norm, centers=k)
return(com$tot.withinss)
})
g1 <- plot(ks, totw)
com <- kmeans(norm, centers=6)
clusters <- com$cluster
clusters <- as.factor(clusters)
names(clusters) <- rownames(norm)
head(clusters)
df <- data.frame(pos, clusters)
g1 <- ggplot(df, aes(x=x, y=y, col=clusters)) + geom_point() +
labs(title = "A: Physical Space", x = "x position", y = "y position")+ theme_minimal()
ggplot(df, aes(x=x, y=y, col=clusters)) + geom_point() +
labs(title = "A: Physical Space", x = "x position", y = "y position") + theme_minimal()
# PCA
pcs <- prcomp(norm)
df <- data.frame(pcs$x,clusters)
ggplot(df, aes(x=PC1, y=PC2, col= clusters)) + geom_point()
# t-SNE
emb <- Rtsne::Rtsne(norm)
df <- data.frame(emb$Y, clusters)
ggplot(df, aes(x = X1, y=X2, col=clusters)) + geom_point() +
labs(title = "B: t-SNE Space") + theme_minimal()
g2 <- ggplot(df, aes(x = X1, y=X2, col=clusters)) + geom_point() +
labs(title = "B: t-SNE Space") + theme_minimal()
g1 + g2
# Cluster 4 unregulated: CD21, CD20, CD1c, CD35, HLA.DR, CD44, CD11c, CD34
# CD21: B cells, DCs
# CD20: B cells
# CD1c: B cells, DCs, monocytes
# HLA.DR: B cells, DCs, macrophages
# CD35: B cells, DC
# CD34: hematopoietic cells
# CD11c: DC, immune cells
# CD44: immune cells, epithelial cells
cellsOfInterest <- names(clusters)[clusters == 4]
otherCells <- names(clusters)[clusters != 4]
results <- sapply(1:ncol(norm), function(i) {
genetest <- norm[,i]
names(genetest) <- rownames(norm)
out <- t.test(genetest[cellsOfInterest], genetest[otherCells], alternative = 'greater')
out$p.value
})
names(results) <- colnames(norm)
results <- sort(results, decreasing = FALSE)
results
df <- data.frame(emb$Y, clusters, gene = norm[, 'CD21'])
g5<- ggplot(df, aes(x=X1, y=X2, col=gene)) + labs(title = "F: CD21 expression on t-SNE Space", col = "CD21") +
scale_color_gradient(low = 'lightgrey', high = 'cyan') + geom_point() + theme_minimal()
df <- data.frame(pos, gene = norm[, 'CD21'])
g6 <- ggplot(df, aes(x = x, y = y, col = gene)) +
labs(title = "E: CD21 expression on Physical Space ",
x = "Cell Position for x",
y = "Cell Position for y",
col = "CD21") + scale_color_gradient(low = 'lightgrey', high = 'cyan') +
geom_point() + theme_minimal()
# Cluster 1 unregulated: SMActin, CD4, CD45, PanCK, CD3e, CD45RO, CD35, CD20, CD44, CD5
# HLA.DR, Podoplanin, CollagenIV, CD21, CD11c, ECAD, CD1c
# SMActin: smooth muscle cells
# CD4: T cells
# CD45: hema cells
# PanCK: epithelial cells
# CD3e: T cell
# CD45RO: memory T cell
# CD35, 20: B cell
# Podoplanin: epidermis and dermis cells
cellsOfInterest <- names(clusters)[clusters == 1]
otherCells <- names(clusters)[clusters != 1]
results <- sapply(1:ncol(norm), function(i) {
genetest <- norm[,i]
names(genetest) <- rownames(norm)
out <- t.test(genetest[cellsOfInterest], genetest[otherCells], alternative = 'greater')
out$p.value
})
names(results) <- colnames(norm)
results <- sort(results, decreasing = FALSE)
results
df <- data.frame(emb$Y, clusters, gene = norm[, 'CD4'])
g3 <- ggplot(df, aes(x=X1, y=X2, col=gene)) + labs(title = "D: CD4 expression on t-SNE Space", col = "CD4") +
scale_color_gradient(low = 'lightgrey', high = 'red') + geom_point() + theme_minimal()
ggplot(df, aes(x=X1, y=X2, col=gene)) + labs(title = "D: CD4 expression on t-SNE Space", col = "CD4") +
scale_color_gradient(low = 'lightgrey', high = 'red') + geom_point() + theme_minimal()
df <- data.frame(pos, gene = norm[, 'CD4'])
g4 <- ggplot(df, aes(x = x, y = y, col = gene)) +
labs(title = "C: CD4 expression on Physical Space ",
x = "Cell Position for x",
y = "Cell Position for y",
col = "CD4") + scale_color_gradient(low = 'lightgrey', high = 'red') +
geom_point() + theme_minimal()
ggplot(df, aes(x = x, y = y, col = gene)) +
labs(title = "C: CD4 expression on Physical Space ",
x = "Cell Position for x",
y = "Cell Position for y",
col = "CD4") + scale_color_gradient(low = 'lightgrey', high = 'red') +
geom_point() + theme_minimal()
g1 + g4 + g6 + g2 + g3 + g5
# Cluster 2 unregulated: CD15, Ki67, CD163
# CD163: macrophage
# Ki67: dividing cells
# CD15: myeloid cells
cellsOfInterest <- names(clusters)[clusters == 2]
otherCells <- names(clusters)[clusters != 2]
results <- sapply(1:ncol(norm), function(i) {
genetest <- norm[,i]
names(genetest) <- rownames(norm)
out <- t.test(genetest[cellsOfInterest], genetest[otherCells], alternative = 'greater')
out$p.value
})
names(results) <- colnames(norm)
results <- sort(results, decreasing = FALSE)
results
#
cellsOfInterest <- names(clusters)[clusters == 3]
otherCells <- names(clusters)[clusters != 3]
results <- sapply(1:ncol(norm), function(i) {
genetest <- norm[,i]
names(genetest) <- rownames(norm)
out <- t.test(genetest[cellsOfInterest], genetest[otherCells], alternative = 'greater')
out$p.value
})
names(results) <- colnames(norm)
results <- sort(results, decreasing = FALSE)
results