Introduction

2023-12-18

The pixelatorR R package provides classes, functions and methods to load, process, analyze and visualize MPX data. It is built on top of the Seurat ecosystem, meaning that it shares the basic object type. Typical Seurat-style analysis workflows can be run as usual, but pixelatorR fills in the gaps where support for MPX data is currently lacking.

Setting up a conda environment

We highly recommend setting up a conda environment to run pixelatorR. With conda, you can create an enviroment using the YAML file provided on our GitHub page. Download the YAML file and run the following from a terminal:

conda env create -f environment.yml

Once the environment is installed, you can activate it with:

conda activate r-pixelator

If you’re using RStudio, you need to make sure that the application uses the correct environment. On Mac OS, you can open RStudio directly from the terminal (where the environment is active) with:

/Applications/RStudio.app/Contents/MacOS/RStudio

Installation

When creating the environment, all required dependencies should be installed. However, pixelatorR is currently only available on GitHub and can be install via remotes or devtools:

remotes::install_github("PixelgenTechnologies/pixelatorR")

Getting started

library(pixelatorR)

On our software website, you can find publicly available data sets. To get started, you can download a PXL file and load it as a Seurat object:

download.file(url = "https://pixelgen-technologies-datasets.s3.eu-north-1.amazonaws.com/mpx-datasets/pixelator/0.12.0/1k-human-pbmcs-v1.0-immunology-I/Sample01_human_pbmcs_unstimulated.dataset.pxl?download=1", 
              destfile = "PBMC_data/Sample01_human_pbmcs_unstimulated.dataset.pxl")

Now, we can load the MPX data as a Seurat object with ReadMPX_Seurat:

seur <- ReadMPX_Seurat("PBMC_data/Sample01_human_pbmcs_unstimulated.dataset.pxl")

## ✔ Created a 'Seurat' object with 477 cells and 80 targeted surface proteins

Seurat v5

In Seurat v5, a new Assay type was introduced called Assay5. Users can switch between using the Assay and Assay5 classes to store counts data by setting the global option Seurat.object.assay.version to either “v3” or “v5”. Similarly, in pixelatorR, you can set the same global option to control whether to use the CellGraphAssay or CellGraphAssay5 class.

The CellGraphAssay class inherits the Assay class and the CellGraphAssay5 class inherits the Assay5 class. These two pixelatorR classes can be thought of as extensions of the Seurat Assay classes as they provide slots to store spatial metrics and MPX component graphs.