iModulonMiner 2.0: multi-modality modularization of prokaryotic bulk, single-cell, and community omics data

Prokaryotic gene expression datasets, including single-cell transcriptomics and community meta-transcriptomics, are growing rapidly, opening discovery opportunities while creating new analytical challenges. Module detection methods, such as independent component analysis, are powerful tools for expression analysis focused on identifying coordinated gene programs that consistently appear within gene expression compendia. Here, we present iModulonMiner 2.0, an end-to-end expression module detection workflow that adds five main capabilities to address emerging multi-dataset, multi-strain, and multi-modality challenges. First, it expands from bulk RNA-seq to support single-cell RNA-seq, community meta-transcriptomics, and multi-omics datasets, along with a standardized data pipeline. Second, it adapts multi-view learning to identify signals that are shared versus context-specific across strains, species, and modalities, supporting cross-context comparison and integration. Third, it provides new robust and prior-guided single-compendium inference methods. Fourth, it adds post-processing diagnostics for interpretability. Fifth, it supports multi-scale transfer of regulatory knowledge from bulk compendia to other modalities. Available as open-source software, iModulonMiner 2.0 provides a unified, module-based workflow for interpreting diverse prokaryotic expression compendia across modalities and biological scales.