Spatially resolved omics enable the discovery of tissue organization of biological or clinical importance. Despite the existence of several methods, performing a rational analysis including multiple algorithms while integrating different conditions such as clinical data is still not trivial. To make such investigations more accessible, we developed mosna, a Python package to analyze spatial omics data in integration with clinical or biological data, providing insight on cell interaction patterns or tissue architecture. mosna is compatible with all spatial omics techniques, it leverages tysserand to build accurate spatial networks, and is compatible with Squidpy. It proposes an analysis pipeline, in which increasingly complex features computed at each step with either the mosna- algorithms or others can be explored in integration with clinical data. The approach produces easy-to-use descriptive statistics and data visualization, while seamlessly training machine learning models and identifying variables with the most predictive power. mosna can take as input any dataset produced by spatial omics methods, including sub-cellular resolved transcriptomics (MERFISH, seqFISH, Xenium) and proteomics (CODEX, MIBI-TOF, low-plex immuno-fluorescence), as well as spot-based spatial transcriptomics (10x Visium, Slide-seq, Stereo-seq). Integration with experimental metadata or clinical data is adapted to binary conditions, such as biological treatments or response status of patients, and to survival data. We demonstrate the proposed analysis pipeline on two spatially resolved proteomic datasets and a spatial transcriptomics dataset containing either binary response to immunotherapy or survival data, and we assess the performance of the proposed niche discovering method in a manually annotated spatial transcriptomic dataset. mosna identifies features describing cellular composition and spatial patterns that can provide biological insight regarding factors that affect response to immunotherapies or survival. AVAILABILITY AND IMPLEMENTATION: mosna is made publicly available to the community, together with relevant documentation at https://mosna-documentation.readthedocs.io/en/latest/index.html and tutorials implemented as Jupyter notebooks to reproduce the result at https://github.com/AlexCoul/mosna.
Coullomb et al. (Sun,) studied this question.