Visualizing Spatial Correlation Between Helper T Cell Markers CCL5 and CD4

1. What data types are you visualizing?

I am visualizing the spatial correlation between expression of the CCL5 and CD4 genes, both known to be overexpressed in immune cells and especially in helper T cells. I illustrate expression of both genes for each cell and spatial position of each cell in the assay.

2. What data encodings (geometric primitives and visual channels) are you using to visualize these data types?

I use the geometric primitive of points to represent each cell. I use the visual channel of a color gradient (from lightblue to red) to encode raw expression of CCL5 and the visual channel of point size to convey raw expression of CD4. I used the color and size visual channels to corepresent gene expression because these are easy to interpret in tandem visually to understand correlation between expression. The visual channel of point position conveys the cell x and y position across the image.

3. What about the data are you trying to make salient through this data visualization?

My data visualization seeks to make more salient the coexpression of CCL5 and CD4 across a spatial array of cells and give insight into immune cell clusters indicated by these markers.

4. What Gestalt principles or knowledge about perceptiveness of visual encodings are you using to accomplish this?

I use the Gestalt principle of similarity to convey comparable gene expression based on my described use of color and size channels for the points to convey raw gene expression. Cells with more similar size and color profiles have comparable levels of expression of the two genes. The Gestalt principle of proximity is perhaps literally encoded with the shown spatial clustering of cells, with the implication that similar cell phenotype in proximity to each other suggests a cluster of similar cells.

5. Code (paste your code in between the ``` symbols)

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data <- read.csv('eevee.csv.gz')

library(ggplot2)


# https://www.antibodies-online.com/areas/immunophenotyping/immune-cell-markers/?srsltid=AfmBOoriZU-Z-eKRbtcb6QTZb4gSSihDNAiMGpiopSolSA3MsV_pAtjz


ggplot(data) + geom_point(aes(x = aligned_x, 
                              y = aligned_y, 
                              col = CCL5, size = CD4)) + 
  scale_color_gradient(low = 'lightblue', high = 'red') + 
  theme_bw()