MoSBi: Automated signature mining for molecular stratification and subtyping

Significance

Molecular patient stratification and disease subtyping are ongoing and high-impact problems that rely on the identification of characteristic molecular signatures. Current computational methods show high sensitivity to custom parameterization, which leads to inconsistent performance on different molecular data. Our new method, MoSBi (molecular signature identification using biclustering), 1) enables so far unmatched high performance for stratification and subtyping across datasets of various different biomolecules, 2) provides a scalable solution for visualizing the results and their correspondence to clinical factors, and 3) has immediate practical relevance through its automatic workflow where individual selection, parameterization, screening, and visualization of biclustering algorithms is not required. MoSBi is a major step forward with a high impact for clinical and wet-lab researchers.

Abstract

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (molecular signature identification using biclustering), an automated multialgorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We systematically evaluated the performance of 11 available and established biclustering algorithms together with MoSBi. For this, we used transcriptomics, proteomics, and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multialgorithm integration, MoSBi identified robust group and disease-specific signatures across all scenarios, overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that supports biological hypothesis generation. MoSBi is available as an R package and web service to make automated biclustering analysis accessible for application in molecular sample stratification.