Merge pull request #149 from acheng41/main

Andrea Cheng (acheng41) HW5 Submission

_posts/2024-02-17-acheng41.md

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+---
+layout: post
+title:  "Identifying KRT8 Expression in Spot-Based Spatial Transcriptomic Data Set"
+author: Andrea Cheng
+jhed: acheng41
+categories: [ HW5 ]
+image: homework/hw5/hw5_acheng41.png
+featured: false
+---
+
+
+In the pikachu Single Cell Data Set, I identified a cluster of cells that had high expression of the KRT8 gene. I hypothesized that these cells were epithelial cells.
+
+In my orginial investigation, I found that the KRT8 gene was almost exclusively upregulated in the cell cluster I identified. Thus, I used expression of this gene to identify a similar cluster of spots in the same general spatial location and pattern with similar upregulation of the KRT8 gene. Panels A and B show the spatial location of the spots of interest as well as the gene expression. Panels C and D show the lower dimension tSNE embedding. And finally, Panels E and F show the lower dimension PCA graph. 
+
+I made slight modifications to my code to identify this cluster. In addition to adjusting which columns I extracted data from, I also used a different k for k-means clustering. I previously performed k-means clustering with k=5. Now I performed k-means clustering with k=4. This is because I found the optimal K based on total withinness to be 4. While the cluster of interest was usually isolated with many different values of k, I found that higher k values seemed to oversegment the other cells. I think there is a lower optimal number of transcriptionally distinct cell-clusters in the spot-based Eevee dataset compared to the single-cell resolution Pikachu dataset because many cells may be in a single spot. Thus, it may be more difficult to identify/distinguish between distinct cell types.  
+
+
+
+```{r}
+#HW5
+#Andrea Cheng (acheng41)
+
+data = read.csv("~/Desktop/GDV class/data/eevee.csv.gz", row.names =1)
+
+#import libraries
+library(Rtsne)
+library(ggplot2)
+library(patchwork)
+
+#design of visualization
+my_color = scale_color_viridis_c(option= "C")
+my_theme = theme(
+  plot.title = element_text(hjust = 0.5, face="bold", size=10),
+  text = element_text(size = 10)
+)
+
+
+data[1:5,1:10]
+pos = data[,2:3]
+head(pos)
+gexp = data[,4:ncol(data)]
+
+#normalize
+gexpnorm = log10(gexp/rowSums(gexp) * mean(rowSums(gexp)) + 1)
+
+
+#identify cell cluster
+df_plot = data.frame(pos, gene = gexpnorm[,'KRT8'])
+#plot on tissue
+gene_tissue = ggplot(df_plot) + 
+  geom_point(aes(x=aligned_x, y = aligned_y, 
+                 col = gene)) + my_color + 
+  labs(col = "Expression of KRT8", 
+       title = "KRT8 - Spacial Visualization")+ my_theme
+
+#PCA
+pcs = prcomp(gexpnorm)
+plot(pcs$sdev[1:20])
+
+#tsne
+emb = Rtsne(pcs$x[,1:20])$Y #faster w/ pcs which represent genes than tSNE on genes 
+df_tsne = data.frame(emb)
+#check tsne plot
+ggplot(df_tsne) + geom_point(aes(x = X1, y = X2)) + theme_classic()
+
+
+#find optimal clustering coefficient k on tSNE
+#look at different tot.within-ness for different k
+results <- sapply(2:15, function(i){
+  com <- kmeans(emb, centers = i)
+  return(com$tot.withinss)
+})
+plot(results, type = 'l')
+
+
+#kmeans with k=4
+com = kmeans(emb, centers = 4)
+
+#visualize clusters
+df_clusters = data.frame(pos, tsne = emb, pcs = pcs$x, kmeans = as.factor(com$cluster))
+
+#Plot showing cluster of interest in tSNE
+kmeans_tsne = ggplot(df_clusters) + 
+  geom_point(aes(x=tsne.1,y = tsne.2, 
+                 col = ifelse(kmeans == 3, "Cluster of Interest", "All Other Cells"))) +
+  labs(
+    col = "Clustering on tSNE", 
+    title = "Cluster of Interest - tSNE Visualization"
+  ) + my_theme
+
+#Plot showing cluster of interest in tissue
+kmeans_tissue = ggplot(df_clusters) + 
+  geom_point(aes(x=aligned_x,y = aligned_y, 
+                 col = ifelse(kmeans == 3, "Cluster of Interest", "All Other Cells")))+
+  labs(
+    col = "Clustering on tSNE", 
+    title = "Cluster of Interest - Spacial Visualization"
+  )+ my_theme
+
+#Plot showing cluster of interest in PCA
+kmeans_pca = ggplot(df_clusters) + 
+  geom_point(aes(x=pcs.PC1,y = pcs.PC2, 
+                 col = ifelse(kmeans == 3, "Cluster of Interest", "All Other Cells"))) +
+  labs(
+    col = "Clustering on tSNE", 
+    title = "Cluster of Interest - PCA Visualization"
+    ) + xlab("PC1") +  ylab ("PC2") + my_theme
+
+#data frame for gene expression of KRT8
+df_gene = data.frame(pos, tsne = emb, pcs = pcs$x, gene = gexpnorm$KRT8)
+
+#Plot showing gene expression in tSNE
+gene_tsne = ggplot(df_gene) + geom_point(aes(x = tsne.1, y=tsne.2, col = gene)) + 
+  my_color + labs(
+    col = "Expression of KRT8", 
+    title = "KRT8 - tSNE Visualization"
+  )+ my_theme
+
+#Plot showing gene expression in PCA
+gene_pca = ggplot(df_gene) + geom_point(aes(x=pcs.PC1,y = pcs.PC2, col = gene)) + 
+  my_color + xlab("PC1") +  ylab ("PC2") + 
+  labs(
+    col = "Expression of KRT8", 
+    title = "KRT8 - PCA Visualization"
+  )+ my_theme
+
+#display plots
+kmeans_tissue + gene_tissue + kmeans_tsne + gene_tsne + kmeans_pca + gene_pca + plot_annotation(tag_levels = 'A')+
+  plot_layout(widths = c(5, 5, 5),
+              guides = "collect",
+              design = "
+              12
+              34
+              56
+              ")
+
+
+```