SPAC: A Python Package for Spatial Single-Cell Analysis of Multiplex Imaging

TL;DR

SPAC is a user-friendly Python package that enables biologists to perform comprehensive spatial analysis of multiplex imaging data, revealing tissue composition and cellular interactions with scalable performance and customizable visualizations.

Contribution

The paper introduces SPAC, a novel Python-based tool designed specifically for biologists to analyze complex spatial single-cell data without extensive coding.

Findings

Streamlines spatial analysis workflow from image segmentation to insights

Provides scalable performance for large datasets

Offers customizable visualizations and specialized spatial statistics

Abstract

Multiplexed immunofluorescence microscopy captures detailed measurements of spatially resolved, multiple biomarkers simultaneously, revealing tissue composition and cellular interactions in situ among single cells. The growing scale and dimensional complexity of these datasets demand reproducible, comprehensive and user-friendly computational tools. To address this need, we developed SPAC (SPAtial single-Cell analysis), a Python-based package and a corresponding shiny application within an integrated, modular SPAC ecosystem (Liu et al., 2025) designed specifically for biologists without extensive coding expertise. Following image segmentation and extraction of spatially resolved single-cell data, SPAC streamlines downstream phenotyping and spatial analysis, facilitating characterization of cellular heterogeneity and spatial organization within tissues. Through scalable performance, specialized spatial statistics, highly customizable visualizations, and seamless workflows from dataset to insights, SPAC significantly lowers barriers to sophisticated spatial analyses.