SegmentaTion AwaRe cLusterING (STARLING)

STARLING is a probabilistic model for clustering cells measured with spatial expression assays (e.g. IMC, MIBI, etc...) while accounting for segmentation errors.

It outputs:

1. Clusters that account for segmentation errors in the data (i.e. should no longer show implausible marker co-expression)
2. Assignments for every cell in the dataset to those clusters
3. A segmentation error probability for each cell

A preprint describing the method and introducing a novel benchmarking workflow is available: Lee et al. (2024) Segmentation error aware clustering for highly multiplexed imaging

A tutorial outlining basic usage is available here.

Requirements

Python 3.9 or 3.10 are required to run starling. If your current version of python is not one of these, we recommend using pyenv to install a compatible version alongside your current one. Alternately, you could use the Docker configuration described below.

Installation

Install with pip

pip install biostarling and then import the module from starling import starling

Building from source

Starling can be cloned and installed locally (typically <10 minutes) via the Github repository,

git clone https://github.com/camlab-bioml/starling.git && cd starling

After cloning the repository, the next step is to install the required dependencies. There are three recommended methods:

1. Use requirements.txt and your own virtual environment:

We use virtualenvwrapper (4.8.4) to create and activated a standalone virtual environment for starling:

pip install virtualenvwrapper==4.8.4
mkvirtualenv starling

For convenience, one can install packages in the tested environment:

pip install -r requirements.txt

The virtual environment can be activated and deactivated subsequently:

workon starling
deactivate

2. Use Poetry and pyproject.toml.

Poetry is a packaging and dependency management tool can simplify code development and deployment. If you do not have Poetry installed, you can find instructions here.

Once poetry is installed, navigate to the starling directory and run poetry install. This will download the required packages into a virtual environment and install Starling in development mode. The location and state of the virtual environment may depend on your system. For more details, see the documentation.

3. Use Docker

If you have Docker installed on your system, you can run docker build -t starling . from the project root in order to build the image locally. You can then open a shell within the image with a command like docker run --rm -it starling bash.

Getting started

With starling installed, please proceed to the online documentation or launch the interactive notebook tutorial to learn more about the package's features.

Authors

This software is authored by: Jett (Yuju) Lee, Conor Klamann, Kieran R Campbell

Lunenfeld-Tanenbaum Research Institute & University of Toronto