phenoptr contains functions that make it easier to read and analyze data tables and images created by Akoya Biosciences' inForm^® software.

phenoptr is part of the Akoya Biosciences Phenoptics™ family of Quantitative Pathology Research Solutions. For more information visit the Phenoptics™ home page.

phenoptr requires the R environment for statistical computing, version 4.0.0 or higher. To install R, visit the R download page. The RStudio IDE is highly recommended as well.

1. Install R. Download the most recent version from https://cloud.r-project.org/.
2. Install RStudio. Download the desktop version from https://www.rstudio.com/products/rstudio/.
3. Start RStudio.
4. Install phenoptr from GitHub. In the RStudio console, copy and paste or type these commands (press Enter after each line):

install.packages("devtools")
devtools::install_github("akoyabio/phenoptr")

1. When requested, enter 1 (Yes) to install BiocInstaller.

• Spatial metrics The Akoya Biosciences rtree package dramatically speeds calculation and reduces memory requirements of spatial metrics such as nearest neighbors and count within. See the installation instructions in the package README file.

These Tutorials introduce the most important features of phenoptr.

• Reading and exploring inForm tables demonstrates reading and processing inForm cell segmentation tables. This is a good place to start.
• Computing inter-cellular distances introduces most of phenoptr's spatial processing capabilities---finding nearest neighbor distances, counting cells within a radius, and visualizing nearest neighbors.
• Find and count touching cells shows how to count touching cells of paired phenotypes.

R is a powerful and popular environment for data manipulation and statistical analysis. Many learning resources are available online.

phenoptr is designed to work in harmony with packages in the tidyverse.

• readr is used to read data files.
• A tibble (also known as data_frame) is the preferred representation of tabular data.
• dplyr, purrr and the pipe operator (%>%) are used extensively in package code and examples.

If you'd like to learn more about the tidyverse packages, a good place to start is Garrett Grolemund and Hadley Wickham's book, available free online at R for data science. If you are new to R, the book's Introduction will help you get started.

See the Reference section of the documentation for details on individual functions.

To cite package phenoptr in publications use:

  Kent S Johnson (2022). phenoptr: inForm Helper Functions. R package version 0.3.2.
  https://akoyabio.github.io/phenoptr/