Merge pull request #108 from dvelazq/main

HW3 Submission

_posts/2024-02-05-dvelazq5.md

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+---
+layout: post
+title:  "The Impact of Normalization on Dimensionality Reduction: IGKC Expression Level Case Study"
+author: Dee Velazquez
+jhed: dvelazq5
+categories: [ HW3 ]
+image: homework/hw3/hw3_dvelazq5.png
+featured: false
+---
+
+### What data types are you visualizing?
+IGKC gene expression count: quantatative
+PC 1 and 2 scores: quantatative
+t-SNE coordinates emb1 and emb2: spatial
+
+I am visualizing the expression of the IGKC gene across PC scores and t-SNE coordinates from dimensionality reduction, and the effect of normalizing and not normalizing
+gene expression data has on dimensionality reduction (for both PCA and t-SNE) and comparing each figure.
+
+### What data encodings (geometric primitives and visual channels) are you using to visualize these data types?
+Geometric Primatives: Points
+
+Visual Channels: Color hues
+
+Each point represents a spot that has a linear combination of the top 100 genes present, and the plasma color hues indicate the expression level of 
+IGKC, the lighter the color meaning a low expression of IGKC, and the darker the color meaning a high expression of IGKC.
+
+### What about the data are you trying to make salient through this data visualization? 
+I am trying to illustrate the effect normalizing gene expression data has on dimensionality reduction, especially on max variances and reduced
+dimensional space positions, and how the visuals change and can impact the way we interpret the relationships between certain points. 
+
+### What Gestalt principles or knowledge about the perceptiveness of visual encodings are you using to accomplish this?
+I used the Gestalt principle of similarity and proximity. Points of the same color are perceived as being part of the same group, in this case, 
+conveying the similarity of spots' IGKC expression levels. Clusters of similar color points close together can be perceived as being part of the same group,
+and ideally have similar gene expression profiles and be of similar cell types.
+
+### What happens if I do or do not normalize/transform the gene expression data (e.g., log and/or scale) prior to dimensionality reduction?
+Without normalizing the gene expression data prior to PCA, the gene with the highest loading factor in PC1 was IGKC and the gene with the highest loading
+factor in PC2 was COL1A1. With normalization, the gene with the highest loading factor in PC1 was IGHA1, and the gene with the highest loading factor in PC2
+was CPB1. This is visually reflected in subplots (a) and (c), where the plots are much more distinct from one another; we see different variances in gene expressions at certain spots, and even more darker points
+in subplot (c) than (a), indicating greater IGKC expression level. It illustrates the importance of normalization, which may more accurately describe our genetic data.
+
+This is also shown again with our t-SNE subplots (b) and (d). We see that without normalizing the gene expression data prior to t-SNE, we get
+very different positions of spots in the reduced-dimensional space. We also see in subplot (d) that there is a higher expression of the IGKC gene than in (b),
+which might accurately represent true IGKC expression in spots and indicate similar cell types.
+
+I think the main takeaway from this is the importance of normalizing data before performing a PCA or t-SNE. Without normalization, features with larger variances may skew results in a PCA. 
+In the case of t-SNE, we measure the distance between points to find the scaled similarity metrics. Without normalization, the distances between points may not accurately represent the true similarities, and can potentially 
+display incorrect information about our genetic data.
+
+```{r}
+# Dee Velazquez
+# HW 3
+
+# Get data
+data <- read.csv('eevee.csv.gz', row.names = 1)
+
+#Get genes
+gexp <- data[,4:ncol(data)]
+
+#Limit number of genes by getting top 100 genes based on expression level
+#COL1A1
+#IGKC
+top_genes <- names(sort(apply(gexp, 2, mean), decreasing=TRUE)[1:100])
+filtered_genes <- gexp[, top_genes]
+# Apply log10 transformation with addition of 1
+
+# BIG QUESTION:
+# Want to know...what happens if I do not normalize gene expression data
+# prior to dimensionality reduction?
+log_filtered_genes <- log10(filtered_genes + 1)
+dim(filtered_genes)
+head(log_filtered_genes)
+gexp_norm <- log10(filtered_genes/rowSums(filtered_genes) *
+                     mean(rowSums(filtered_genes))+1)
+dim(gexp_norm)
+head(gexp_norm)
+
+## Dimensionalality reduction w/o normalizing gene expression beforehand
+# PCA
+pca <- prcomp(filtered_genes)
+?prcomp
+dim(pca$rotation)
+#IGKC is the gene with the highest loading value for PC1
+head(sort(pca$rotation[,1], decreasing=TRUE))
+#COL1A1 is the gene with the highest loading value for PC2
+head(sort(pca$rotation[,2], decreasing=TRUE))
+head(pca$x[,1:5])
+head(pca$rotation[,1:5])
+df <- data.frame(pca$x, filtered_genes)
+df$IGKC_log10 <- log10(df$IGKC + 1)
+#p1 <- ggplot(df) + geom_point(aes(x = PC1, y= PC2, col=IGKC))
+#+ theme_minimal()
+p1 <- ggplot(df) + geom_point(aes(x = PC1, y= PC2, col=IGKC_log10), alpha = 0.7) +
+  scale_color_viridis_c(option = "C",  name = "log10(IGKC Expression)", direction = -1) +
+  labs( title = "PCA of the IGKC Gene At Each Spot Without Normalization",
+    x = "PC1",
+    y = "PC2") + theme_minimal()
+p1
+#tSNE
+emb <- Rtsne(filtered_genes, dims = 2)
+?Rtsne
+df2 <- data.frame(emb=emb$Y,filtered_genes)
+df2$IGKC_log10 <- log10(df$IGKC + 1)
+p2 <-ggplot(df2) + geom_point(aes(x= emb.1, y =emb.2,col=IGKC_log10), alpha = 0.7) +
+  scale_color_viridis_c(option = "C",  name = "log10(IGKC Expression)", direction = -1) +
+  labs(title = "t-SNE of the IGKC Gene At Each Spot Without Normalization",
+  x = "EMB1",
+  y = "EMB2") + theme_minimal()
+p2
+
+## Dimensionalality reduction done after normalized gene expression
+# PCA
+#gexp_norm
+#pca2 <- prcomp(log_filtered_genes)
+pca2 <- prcomp(gexp_norm)
+head(sort(pca2$rotation[,1], decreasing=TRUE))
+#now...IGHA1 is the highest LF for PC1 instead of IGKC
+head(sort(pca2$rotation[,2], decreasing=TRUE))
+#now...CPB1 is the highest LF for PC2 instead of COL1A1
+head(pca2$x[,1:5])
+head(pca2$rotation[,1:5])
+#df3 <- data.frame(pca2$x, log_filtered_genes)
+df3 <- data.frame(pca2$x, gexp_norm)
+p3 <- ggplot(df3) + geom_point(aes(x = PC1, y= PC2, col=IGKC), alpha = 0.7) +
+  scale_color_viridis_c(option = "C",  name = "log10(IGKC Expression)", direction = -1) +
+  labs(title = "PCA of the IGKC Gene At Each Spot With Normalization",
+  x = "PC1",
+  y = "PC2") + theme_minimal()
+p3
+#tSNE
+#emb2 <- Rtsne(log_filtered_genes, dims = 2)
+emb2 <- Rtsne(gexp_norm, dims = 2)
+#df4 <- data.frame(emb=emb2$Y,log_filtered_genes)
+df4 <- data.frame(emb=emb2$Y,gexp_norm)
+p4 <- ggplot(df4) + geom_point(aes(x = emb.1, y =emb.2, col=IGKC), alpha = 0.7) +
+  scale_color_viridis_c(option = "C",  name = "log10(IGKC Expression)", direction = -1) +
+  labs(title = "t-SNE of the IGKC Gene At Each Spot With Normalization",
+  x = "EMB1",
+  y = "EMB2") + theme_minimal()
+p4
+# Plotting
+p1 + p3 + plot_annotation(tag_levels = 'a') + plot_layout(ncol = 1)
+p2 + p4 + plot_annotation(tag_levels = 'a') + plot_layout(ncol = 1)
+final_plot <- p1 + p2 + p3 + p4 + plot_annotation(tag_levels = 'a') + plot_layout(ncol = 2)
+final_plot
+
+```