PackageBluishgreen

The goal of PackageBluishgreen is to package the internals for clustering cells for Olesja Popow (pronounced “po-pow”). The cells were identified using a separate algorithm which output DAPI and FITC values for each cell into a CSV. This package maintains this data in a data structure called tissue_slide and manages any classification methods applied to the cells.

Installation

You can install the released version of ‘PackageBluishgreen’ from GitHub with:

#> If using 'renv'
renv::install("Kevin-Haigis-Lab/PackageBluishgreen")
#> else
devtools::install_github("Kevin-Haigis-Lab/PackageBluishgreen")

The full documentation can be found here. For examples, check out the vignettes.

If there is a specific classification method you would like, please open an issue on GitHub.

Example usage

The tissue slide data structure

The tissue slide class is designed to hold three things:

  1. the signal intensity data from a microscopy slide
  2. metadata for the samples
  3. classification methods and results

A new tissue slide can be created by just passing in the slide data.

pancreas_data <- read.csv(system.file(
  "extdata",
  "unmicst-OP1181_pancreas_TUNEL_01.csv",
  package = "PackageBluishgreen"
))

pancreas_data <- pancreas_data[, c(1, 3:5)]
colnames(pancreas_data) <- c("cell_id", "fitc", "x", "y")

pancreas_slide <- tissue_slide(pancreas_data, metadata = list(tissue = "pancreas", mouse = "OP1181"))

The metadata can be easily retrieved.

get_slide_metadata(pancreas_slide)
#> $tissue
#> [1] "pancreas"
#> 
#> $mouse
#> [1] "OP1181"

It is also very easy to plot the data.

plot_tissue(pancreas_slide, color = log10(fitc))

Manual classification

A more thorough guide can be found in the “Manual classification” vignette.

The cluster_manually() function should be used to apply a manual classification cutoff to the data.

pancreas_slide <- cluster_manually(pancreas_slide, fitc, 4.3, transform = log10)

The results can be easily plotted.

plot_slide_clusters(pancreas_slide, method = "manual")

A summary of the results can be obtained using the summarize_cluster_results() function.

summarize_cluster_results(pancreas_slide)
#> # A tibble: 2 x 2
#>   manual_cluster     n
#> * <fct>          <int>
#> 1 1              66244
#> 2 2                787

Mistakes or questions? Open an issue on Github.