biscot: an Optimal Transport framework for multimodal bacterial single-cell data analysis

Computational optimal transport-based approaches have emerged as promising tools for the integration and interpretation of complex single-cell data. In this study, we introduce an integrative Optimal Transport (OT) framework for spatiotemporal and multi-omics bacterial single-cell analysis using Gaussian Mixture Model (GMM) OT, termed biscot (bacterial integrative single-cell optimal transport). We show that biscot, equipped with a novel global-to-local GMM initialization, outperforms classical OT and entropically-regularized OT methods both in terms of speed and accuracy for disentangling complex bacterial communities mixtures from single-cell flow cytometry data. When applied to time-series flow cytometry data from Bacillus subtilis, our framework delivers robust and biologically meaningful results, effectively capturing subtle phenotypic shifts in spore populations transitioning from inactive to active growth states. biscot also allows multi-omics integration of flow cytometry and unpaired bacterial single-cell RNA sequencing (scRNA-seq) data, enabling the alignment of individual gene expression profiles to the cytometric data. For an unpaired flow cytometry/scRNA-seq dataset of Bacillus subtilis cells, we validate the biological plausibility of inferred gene expression patterns with relevant marker genes, including spoVID and nin, closely aligning with observed cellular states. Overall, our framework thus provides not only dynamic tracking of phenotypic cell states but aligns cell states with detailed transcriptomic information from scRNA-seq, demonstrating its potential to advance microbial single-cell research. biscot will be made publicly available on GitHub.