Relationship between expression of LUM and POSTN

1. What data types are you visualizing?

I am visualizing quantitative data of the expression count of the POSTN and LUM gene for each cell, and the quantitative data of the area for each cell.

2. 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 expression count of the LUM gene, I am using the visual channel of position along the y axis. To encode expression count of the POSTN gene, I am using the visual channel of position along the x axis. To encode the area for each cell, I am using the visual channel of saturation going from an unsaturated lightgrey to a saturated red. I thought about using size to encode the area for each cell since it is more intuitive to think about the size of the dot in terms of the cells’ area. However, it became hard to see since the dots are still very close to one another.

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

My data visualization seeks to make more salient a. The positive correlation between POSTN, LUM expression, and the cell area. b. Smaller cells seem to have lower expression of POSTN and LUM.

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

a. I am using the principle of Continuity by adding a line of regression on to the plot which shows positive correlation between POSTN and LUM expressions. b. I am also using the principle of Similarity as cells with similar cell area has similar hue.

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

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file <- "data/pikachu.csv.gz"
data <- read.csv(file)

ggplot(data) +geom_point(aes(x=POSTN,y=LUM,  color=cell_area))+
  labs(title = "Relationship between expression of LUM and POSTN",
       x= 'POSTN expression',
       y='LUM expression') +
  scale_color_gradient(low='lightgrey',high='red') +
  geom_abline(intercept = intercept, slope = slope, color="blue",  
              linetype="dashed", size=1.5)

Code to generate regression line adapted from https://www.geeksforgeeks.org/add-regression-line-to-ggplot2-plot-in-r/