UnPaSt: unsupervised patient stratification by biclustering of omics data

TL;DR

UnPaSt is a new biclustering algorithm designed for unsupervised patient stratification, effectively identifying disease subtypes and biological patterns in diverse omics datasets, surpassing existing methods especially in complex scenarios.

Contribution

The paper introduces UnPaSt, a novel biclustering method that improves unsupervised patient stratification and biological pattern detection across multiple omics data types.

Findings

UnPaSt outperforms existing methods in breast cancer and asthma subtype identification.

It detects biologically meaningful patterns in various high-throughput datasets.

UnPaSt provides more interpretable insights into data heterogeneity.

Abstract

Unsupervised patient stratification is essential for disease subtype discovery, yet, despite growing evidence of molecular heterogeneity of non-oncological diseases, popular methods are benchmarked primarily using cancers with mutually exclusive molecular subtypes well-differentiated by numerous biomarkers. Evaluating 22 unsupervised methods, including clustering and biclustering, using simulated and real transcriptomics data revealed their inefficiency in scenarios with non-mutually exclusive subtypes or subtypes discriminated only by few biomarkers. To address these limitations and advance precision medicine, we developed UnPaSt, a novel biclustering algorithm for unsupervised patient stratification based on differentially expressed biclusters. UnPaSt outperformed widely used patient stratification approaches in the de novo identification of known subtypes of breast cancer and asthma. In addition, it detected many biologically insightful patterns across bulk transcriptomics, proteomics, single-cell, spatial transcriptomics, and multi-omics datasets, enabling a more nuanced and interpretable view of high-throughput data heterogeneity than traditionally used methods.