Intrinsic-dimension analysis for guiding dimensionality reduction and data fusion in multi-omics data processing
Jessica Gliozzo, Mauricio Soto, Valentina Guarino, Arturo Bonometti, Alberto Cabri, Emanuele Cavalleri, Justin Reese, Peter N. Robinson, Marco Mesiti, Giorgio Valentini, Elena Casiraghi
Abstract
Multi-omics data have revolutionized biomedical research by providing a comprehensive understanding of biological systems and the molecular mechanisms of disease development. However, analyzing multi-omics data is challenging due to high dimensionality and limited sample sizes, necessitating proper data-reduction pipelines to ensure reliable analyses. Additionally, its multimodal nature requires effective data-integration pipelines. While several dimensionality reduction and data fusion algorithms have been proposed, crucial aspects are often overlooked. Specifically, the choice of projection space dimension is typically heuristic and uniformly applied across all omics, neglecting the unique high dimension small sample size challenges faced by individual omics. This paper introduces a novel multi-modal dimensionality reduction pipeline tailored to individual views. By leveraging intrinsic dimensionality estimators, we assess the curse-of-dimensionality impact on each view and propose a two-step reduction strategy for significantly affected views, combining feature selection with feature extraction. Compared to traditional uniform reduction pipelines in a crucial and supervised multi-omics analysis setting, our approach shows significant improvement. Additionally, we explore three effective unsupervised multi-omics data fusion methods rooted in the main data fusion strategies to gain insights into their performance under crucial, yet overlooked, settings. • Unbiased id estimation: We address the overlooked problem of intrinsic dimensionality ( id ) in multi-omics data, proposing a principled approach for unbiased id estimation. • Principled dimensionality reduction: A tailored DR approach ensures robust multi-modal data characterization and effectively mitigates information loss. • View-specific DR pipeline: Our block-analysis framework customizes DR to each view with a novel two-step strategy for improved dimensionality reduction. • Comprehensive evaluation: Testing nine TCGA cancer datasets reveals the impact of proper DR on multi-omics integration and predictive performance. • Improving data fusion and predictions: Our DR pipeline enhances fusion algorithms and achieves better survival prediction accuracy using interpretable classifiers.