Using scatterbar with Visium data

Below is how to use scatterbar from the provided Visium dataset of an FFPE preserved adult mouse brain partial coronal section from 10X Genomics.

library(scatterbar)
library(ggplot2)
data("adult_mouse_brain_ffpe")

plot(adult_mouse_brain_ffpe$pos)

head(adult_mouse_brain_ffpe$prop)
#>                            1          2         3         4         5         6
#> AAACAGAGCGACTCCT-1 0.1264103 0.00000000 0.0000000 0.2183626 0.0000000 0.0000000
#> AAACCCGAACGAAATC-1 0.0000000 0.00000000 0.0000000 0.0000000 0.1612265 0.7494948
#> AAACCGGGTAGGTACC-1 0.2881994 0.32242398 0.0000000 0.3893766 0.0000000 0.0000000
#> AAACCGTTCGTCCAGG-1 0.3040547 0.27785934 0.2535515 0.0000000 0.0000000 0.0000000
#> AAACGAAGAACATACC-1 0.1585737 0.09403518 0.0000000 0.3510139 0.0000000 0.0000000
#> AAACGAGACGGTTGAT-1 0.0000000 0.00000000 0.2962469 0.0000000 0.0000000 0.0000000
#>                            7 8          9 10         11         12
#> AAACAGAGCGACTCCT-1 0.1865617 0 0.07496736  0 0.39369799 0.00000000
#> AAACCCGAACGAAATC-1 0.0000000 0 0.00000000  0 0.00000000 0.08927876
#> AAACCGGGTAGGTACC-1 0.0000000 0 0.00000000  0 0.00000000 0.00000000
#> AAACCGTTCGTCCAGG-1 0.0768548 0 0.00000000  0 0.08767969 0.00000000
#> AAACGAAGAACATACC-1 0.2808292 0 0.00000000  0 0.11554800 0.00000000
#> AAACGAGACGGTTGAT-1 0.0000000 0 0.00000000  0 0.00000000 0.70375311

start.time <- Sys.time()
scatterbar(
  adult_mouse_brain_ffpe$prop,
  adult_mouse_brain_ffpe$pos,
  size_x = 220,
  size_y = 220,
  legend_title = "Cell Types"
) + coord_fixed()
#> Calculated size_x: 220
#> Calculated size_y: 220
#> Applied padding_x: 0
#> Applied padding_y: 0


end.time <- Sys.time()
print(end.time - start.time)
#> Time difference of 0.1058111 secs

Just like with the mOB data, we can change the order of how each bar is laid out by changing the order of the cell-type proportion matrix and combine scatterbar with other ggplot geoms and customization.

start.time <- Sys.time()
custom_colors <- c('1'= '#5d6f99',
    '2' = '#985a39',
    '3' =  '#d6589a',
    '4' = '#4d1395',
    '5' = '#b5ef27',
    '6' = '#77d5bc',
    '7' = '#7830d2',
    '8' ='#b43b59',
    '9' = '#1c40b1',
    '10' = "#FF5733",
    '11' = '#FFFF00',
    '12' = '#f4a6f1')
scatterbar::scatterbar(adult_mouse_brain_ffpe$prop[, c(2,3,4,11,5,6,10,7,8,1,9, 12)], adult_mouse_brain_ffpe$pos, size_x = 220, size_y = 220, padding_x = 0.1, padding_y = 0.1, legend_title = 'Cell Type', colors = custom_colors) +
  geom_point(data=adult_mouse_brain_ffpe$pos, mapping=aes(x=x, y=y), size = 0.1) +
  theme_bw() + ylab('y') + ggplot2::coord_fixed()
#> Calculated size_x: 219.9
#> Calculated size_y: 219.9
#> Applied padding_x: 0.1
#> Applied padding_y: 0.1

end.time <- Sys.time()
print(end.time - start.time)
#> Time difference of 0.1192179 secs

Welcome to ClientVPS Mirrors

Using scatterbar with Visium data

Dee Velazquez and Jean Fan

2024-11-22

Using scatterbar with Visium data