This fork of phenoptr contains altering source changes from the source material.
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.
- Install R. Download the most recent version from https://cloud.r-project.org/.
- Install RStudio. Download the desktop version from https://www.rstudio.com/products/rstudio/.
- Start RStudio.
- Install
phenoptrfrom GitHub. In the RStudio console, copy and paste or type these commands (press Enter after each line):
# install devtools package
install.packages("devtools")
# install phenoptr plugin from GitHub
devtools::install_github("christianrickert/phenoptr")
- When requested, enter
1(Yes) to install BiocInstaller.
- Spatial metrics
The Akoya Biosciences
rtreepackage 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/