Merge pull request #204 from archana64/main

hwEC1_abalan2

_posts/2024-03-06-abalan2.md

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+---
+layout: post
+title:  "Comparing the effect of tSNE on varying number of PCs:KRT7 
+expression (using gganimate)"
+author: Archana Balan
+jhed: abalan2
+categories: [ HW EC1 ]
+image: homework/hwEC1/hwEC1_abalan2.gif
+featured: false
+---
+### If I perform non-linear dimensionality reduction on PCs, what happens when I vary how many PCs should I use?
+
+I am visualizing the effect of performing tSNE on varying number of PCs. Similar to my homework 3 visualization I have visulaized the 
+KRT7 gene expression in the reduced dimensionality space. The gene expression was normalized (by total gene 
+expression for each cell) prior to PCA. t-SNE was then performed by using all PCs, first 30 PCs only, first 10 PCs only, first 4 PCs only and first 2PCs only. Using gganimate I have shown the transition of performing tSNE starting with all PCs 
+and then reduced number of PCs. The animation helps emphasize the difference in the embeddings as we reduce the number of PCs.
+
+
+
+
+```{r}
+library(dplyr)
+library(tidyverse)
+library(ggplot2)
+library(Rtsne)
+library(patchwork)
+library(viridis)
+library(gganimate)
+
+
+## Data Description
+# Each row is a cell ~17000
+# Columns are mainly genes ~300
+
+# Set working directory path and read in data
+setwd("/Users/archanabalan/Karchin Lab Dropbox/Archana Balan/Coursework/Spring2024/GDV/homework/")
+data = read.csv("./data/pikachu.csv.gz", row.names =1)
+
+# Drop first 5 columns for gene expression (cell_id, cell_area etc)
+gexp = data[, -c(1:5)]
+rownames(gexp) <- data$cell_id
+
+# normalize data by total gene expression for each cell
+gexpnorm = gexp/rowSums(gexp)
+# check if normalization is correct (value would be 1 for all cells)
+unique(rowSums(gexpnorm) )
+
+
+# PCA using prcomp function
+# Center = TRUE and scale = FALSE params
+pcs = prcomp(gexpnorm, center = TRUE, scale. = FALSE)
+
+# tSNE on first 2 PCs
+tsne_2 = Rtsne(pcs$x[, 1:2], dims = 2, pca = FALSE)
+
+# tSNE on first 4 PCs
+tsne_4 = Rtsne(pcs$x[, 1:4], dims = 2, pca = FALSE)
+
+# tSNE on first 10 PCs
+tsne_10 = Rtsne(pcs$x[, 1:10], dims = 2, pca = FALSE)
+
+# tSNE on first 30 PCs
+tsne_30 = Rtsne(pcs$x[, 1:30], dims = 2, pca = FALSE)
+
+# tSNE on all PCs
+tsne_all = Rtsne(pcs$x, dims = 2, pca = FALSE)
+
+
+plot.df <- rbind(data.frame(tsne_x = tsne_2$Y[,1],
+                 tsne_y = tsne_2$Y[,2], 
+                 KRT7 = gexpnorm$KRT7, 
+                 order = "TSNE on first 2 PCS"),
+            data.frame(tsne_x = tsne_4$Y[,1],
+                       tsne_y = tsne_4$Y[,2], 
+                       KRT7 = gexpnorm$KRT7, 
+                       order = "TSNE on first 4 PCS"), 
+            data.frame(tsne_x = tsne_10$Y[,1],
+                       tsne_y = tsne_10$Y[,2], 
+                       KRT7 = gexpnorm$KRT7, 
+                       order = "TSNE on first 10 PCS"),
+            data.frame(tsne_x = tsne_30$Y[,1],
+                       tsne_y = tsne_30$Y[,2], 
+                       KRT7 = gexpnorm$KRT7, 
+                       order = "TSNE on first 30 PCS"),
+            data.frame(tsne_x = tsne_all$Y[,1],
+                       tsne_y = tsne_all$Y[,2], 
+                       KRT7 = gexpnorm$KRT7, 
+                       order = "TSNE on all PCS")) %>% 
+            mutate(order = factor(order, levels = rev(unique(order))))
+
+
+# Ref: https://stackoverflow.com/questions/37397303/change-label-of-gganimate-frame-title
+
+p1 <- ggplot(data = plot.df, aes (x=tsne_x, y = tsne_y, col = KRT7) ) +
+  geom_point(size =0.9) +
+  theme_classic() + 
+  scale_color_viridis(option="plasma") +
+  labs(x = "X", y = "Y") 
+
+anim <- p1 + 
+  transition_states(order) +
+  view_follow() +
+  labs(title = '{closest_state}') 
+
+# Ref: https://stackoverflow.com/questions/52244347/control-speed-of-a-gganimation
+
+animate(anim, height = 400, width = 400, fps = 5)
+
+anim_save("hwEC1_abalan2.gif", animation = last_animation(), path = "./")
+
+
+
+```
+