README.md

In PerkinElmer/phenoptr: inForm Helper Functions

phenoptr - Helpers for working with inForm data

Important update

The PerkinElmer Phenoptics team is now part of Akoya Biosciences. For the latest version of phenoptr please visit https://akoyabio.github.io/phenoptr/. For information about the acquisition, see this press release.

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

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

Installation

phenoptr requires the R environment for statistical computing, version 3.3.0 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("PerkinElmer/phenoptr", build_vignettes=TRUE)

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

Getting Started

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.

For examples showing aggregation across multiple inForm fields and multiple slides, see the Tutorials in the phenoptrExamples package.

Learning R

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.

Full documentation

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

To cite package phenoptr in publications use:

  Kent S Johnson (2018). phenoptr: inForm Helper Functions. R package version 0.1.1.9007.   https://perkinelmer.github.io/phenoptr/

PerkinElmer/phenoptr documentation built on May 30, 2019, 8:01 a.m.