SPRM - Spatial Process & Relationship Modeling

Ted Zhang, Haoran Chen, Matt Ruffalo, and Robert F. Murphy
Ray and Stephanie Lane Computational Biology Department
School of Computer Science, Carnegie Mellon University
V1.5.5 October 1, 2025

Description

SPRM is a statistical modeling program that is used in the HuBMAP project to calculate a range of metrics, descriptors/features and models from multichannel tissue images. It requires at a minimum a multichannel tissue image and a corresponding indexed image containing cell segmentations. The metrics measure the quality of the provided images and the quality of the provided cell segmentation. The descriptors are used for clustering of cells using various approaches and are saved for use in comparison with other tissue images and for content-based image search.

SPRM can be used standalone with any type of multichannel 2D or 3D image (e.g., CODEX, IMS) for which a cell segmentation is available.

Inputs

Primary: Two OMETIFF files -

1. a 3D or 4D multichannel intensity Image (2D or 3D for multiple channels)
2. a corresponding 3D or 4D Indexed Image containing one channel for each component of cell segmentation (currently “cells”, “nucleus”, “cell membrane” and “nuclear membrane”).

Execution: (assuming SPRM.py is in working directory)

[python_path] SPRM.py --img-dir [img_dir_path] --mask-dir [mask_dir_path] --optional-img-dir [optional_img_dir_path] --output_dir [output_dir_path] --options_path [options_file_path] --celltype_labels [labels_file] --processes [number_of_processes_to_use]

Outputs

OME-TIFFs showing pixel level results (remapping of channels) [3 per input image] CSV containing interpolated cell outlines & polygons [2 per input image] CSVs containing features for each cell [4 per image] CSVs containing features for subcellular components segmentation [12 per image] CSV containing clustering results for each cell (row) for different methods (column) [1 per input image] CSVs containing mean values of “markers” for each cluster for each clustering method [5 per input image] PNGs showing each cell colored by cluster for each clustering method [7 per input image] CSV containing the signal to noise ratios of the image per channel [1 per input image] CSV containing PCA and Silhouette analysis of the image [2 per input image] JSON containing all features and cluster assignments

Simple illustration

The demo folder contains two simple ways to run SPRM. For both, start by

• downloading/cloning this repository

Install all dependencies:

Automated

• Run the following command

./install_sprm.sh

• Will create an environment called SPRM if you have conda or pyenv otherwise will install in base environment.

Manual

• in the main folder, run the command

pip install .

Change to the demo folder ("cd demo") and you can then either:

• Use the shell script run_sprm.sh which will just run SPRM using the downloaded demo image files. It will place the outputs in the folder sprm_demo_outputs and write a log of the messages from SPRM into the file sprm_demo_outputs/sprm_demo_outputs.log.

or

• Activate the SPRM environment (e.g., "conda activate SPRM") and then start jupyter notebook ("jupyter notebook"). Open the sprm_demo.ipynb notebook which will run SRPM on the demo files and then display the outputs in the notebook.

EXAMPLES

We have provided you with example images and masks but feel free to use your own as well!

• downloading the demo image files from this link and putting them in the "demo" folder into their own respective directories "img" and "mask".

Prerequisites

• Python 3.8 or newer
• AICSImageIO
• Matplotlib
• Numba
• NumPy
• Pandas
• Pillow
• Pint
• scikit-learn
• SciPy
• Shapely

Documentation

For more information on specific analytical tools and outputs of SPRM:

Documentation

Development information

Code in this repository is formatted with black and isort.

A pre-commit hook configuration is provided, which runs black and isort before committing. Run pre-commit install in each clone of this repository which you will use for development (after pip install pre-commit into an appropriate Python environment, if necessary).

Contact

Robert F. Murphy - murphy@cmu.edu
Ted (Ce) Zhang - tedz@andrew.cmu.edu