ArticleBriefings in bioinformatics2026
SOPA and SIMPA: normalized single-sample integrated multiomics pathway analysis of tumor heterogeneity in solid cancers.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Inter-sample tumor heterogeneity poses significant challenges to metastatic cancer treatment. Although multiomics analyses provide nuanced molecular insights into tumor heterogeneity, current molecular pathway analysis tools focus on group-based comparisons, which may overlook differential single-sample perturbations. Here, we present normalized single-sample single-omic pathway analysis (SOPA), and its extension, normalized single-sample integrated multiomics pathway analysis (SIMPA), as a bioinformatics pipeline for performing supervised differential pathway analysis. The pipeline utilizes custom algorithms to analyze differential pathway activity in single samples, comparing the molecular profile in each sample to a range of controls. In single -omics analysis, SOPA shows advantages compared to standard tools such as single sample gene set enrichment analysis and gene set variation analysis in identifying single sample deviations from predefined controls. For integrated multiomics, we show that in predefined-control contexts, SIMPA provides an effective alternative over unsupervised tools such as multiomics gene set analysis (MOGSA) and PAthway Deviation scores using Multiple Factor Analysis (padma), addressing tumor heterogeneity. Particularly, SIMPA unveiled particular tumor subgroups with dysregulated immune and metabolic pathway activity marked by variable immune infiltration and survival differences, which were missed by MOGSA and padma. Overall, SOPA and SIMPA are valuable in the supervised analysis of single samples and allow for the investigation of complex multiomics data to gain personalized hypothesis-generating insights. The flexibility of this pipeline allows implementation in preclinical and clinical research, offering significant advantages over prior pathway analysis tools in studying systems biology. The Python package for SOPA and SIMPA is freely accessible at https://github.com/hasanalsharoh/SIMPApy/.
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