Merge pull request #45 from jemoore16/main

homework 1

_posts/2023-01-28-jmoor185.md

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+---
+layout: post
+title:  "Spacial gene expression of CTLA4 and CDH1"
+author: Jamie Moore
+jhed: jmoor185
+categories: [ HW1 ]
+image: homework/hw1/hw1_jmoor185.png
+featured: false
+---
+
+### What data types are you visualizing?
+I am encoding spatial data because you can see the genes being expressed in the cell via the x,y alignment.
+I am also encoding quantitive data by the scale of how many cells are expressing the genes.
+
+### What data encodings (geometric primitives and visual channels) are you using to visualize these data types?
+I am using the geometric primitive of points to represent each cell. 
+To encode the expression count of the CTLA4 and CDH1 gene, I am using the visual channel of color hue and saturation, going from an unsaturated light grey to a saturated purple. 
+To encode the spatial x position, I am using the visual channel of position along the x-axis. 
+To encode the spatial y position, I am using the visual channel of position along the y-axis.
+
+### What about the data are you trying to make salient through this data visualization? 
+My data visualization seeks to make more salient the relationship between CTLA4 and CDH1 expression and the cell positions. 
+
+### What Gestalt principles or knowledge about perceptiveness of visual encodings are you using to accomplish this?
+I used the Gestalt principles of similarity, proximity, and enclosure to represent the CTLA4 and CDH1 expression within the cell(s) that are aligned spatially.
+I encode the Gesalt principle of similarity of like cells expressing CTLA4/CDH1 by being colored purple/a saturation of purple. 
+I encode the Gestalt principle of the proximity of the genes being expressed near one another, meaning the cells can be categorized as potentially cancerous cells. 
+I encode the Gesalt principle of enclosure because you can see outlines of cells and if they express the genes CTLA4/CDH1 or not. 
+
+
+```{r}
+# I used https://www.nationalbreastcancer.org/other-breast-cancer-genes/ to find breast cancer genes within the gene list provided in the data csv
+# I used https://stackoverflow.com/questions/1249548/side-by-side-plots-with-ggplot2 for the side by side plots
+
+file <- '~/Desktop/genomic-data-visualization-2024/data/pikachu.csv.gz'
+data <- read.csv(file, row.names=1)
+
+library(ggplot2)
+install.packages("gridExtra")
+library("gridExtra")
+
+p1 <- ggplot(data) + geom_point(aes(x = aligned_x, y = aligned_y, col = CTLA4), size = 0.01)
+    + scale_colour_gradient(low = 'lightgrey', high = 'blue') + theme_minimal()
+p2 <- ggplot(data) + geom_point(aes(x = aligned_x, y = aligned_y, col = CDH1), size = 0.01)
+    + scale_colour_gradient(low = 'lightgrey', high = 'blue') + theme_minimal()
+grid.arrange(p1,p2)
+
+```