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Getting started with scatterbars

Dee Velazquez and Jean Fan

2024-11-22

Getting started with scatterbars

Below is how to use scatterbar from the provided spatial transcriptomic data from the mouse olfactory bulb tissue sample.

library(scatterbar)
data("mOB")

plot(mOB$xy)

head(mOB$data)
##                            1          2         3          4         5
## ACAACTATGGGTTGGCGG 0.1420916 0.05540521 0.2902388 0.00000000 0.1657935
## ACACAGATCCTGTTCTGA 0.0000000 0.77541118 0.0000000 0.00000000 0.0000000
## ACATCACCTGCGCGCTCT 0.3360811 0.00000000 0.0000000 0.32262562 0.3412932
## ACATTTAAGGCGCATGAT 0.2239877 0.00000000 0.0000000 0.55189741 0.2241149
## ACCACTGTAATCTCCCAT 0.1373168 0.08529107 0.2204332 0.00000000 0.0000000
## ACCAGAGCCGTTGAGCAA 0.1364591 0.37986322 0.1499492 0.06702756 0.2147332
##                             6         7          8
## ACAACTATGGGTTGGCGG 0.00000000 0.1329341 0.21353679
## ACACAGATCCTGTTCTGA 0.14536750 0.0000000 0.07922131
## ACATCACCTGCGCGCTCT 0.00000000 0.0000000 0.00000000
## ACATTTAAGGCGCATGAT 0.00000000 0.0000000 0.00000000
## ACCACTGTAATCTCCCAT 0.00000000 0.1876818 0.36927715
## ACCAGAGCCGTTGAGCAA 0.05196769 0.0000000 0.00000000
start.time <- Sys.time()
scatterbar::scatterbar(mOB$data, mOB$xy) + ggplot2::coord_fixed()
## Calculated size_x: 1.24034734589208
## Calculated size_y: 0.930260509419063
## Applied padding_x: 0
## Applied padding_y: 0

end.time <- Sys.time()
print(end.time - start.time)
## Time difference of 0.04842401 secs

We can change the order of how each bar is laid out by changing the order of the cell-type proportion matrix of spatial transcriptomic data. We can also combine scatterbar with other ggplot geoms and customization!

library(ggplot2)
start.time <- Sys.time()
scatterbar::scatterbar(mOB$data[, c(2,3,4,5,6,7,8,1)], mOB$xy, size_x = 1, size_y = 1, padding_x = 0.1, padding_y = 0.1) +
  geom_point(data=mOB$xy, mapping=aes(x=x, y=y)) +
  theme_bw() + ylab('y') + ggplot2::coord_fixed()
## Calculated size_x: 0.9
## Calculated size_y: 0.9
## Applied padding_x: 0.1
## Applied padding_y: 0.1

end.time <- Sys.time()
print(end.time - start.time)
## Time difference of 0.09683204 secs

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.