Probabilistic pathway-based multimodal factor analysis

• DOI: 10.1093/bioinformatics/btae216
• Publication Date: 2024-06-28T09:25:58Z
• Authors: Alexander Immer; Stefan G Stark; Francis Jacob; Ximena Bonilla; Tinu Thomas; André Kahles; Sandra Goetze; Emanuela S Milani; Bernd Wollscheid; ; Rudolf Aebersold; Melike Ak; Faisal S Al-Quadd...; Silvana I Albert; Jonas Albinus; Ilaria Alborelli; Sonali Andani; Per-Olof Attinger; Marina Bacac; Daniel Baumhoer; Beatrice Beck-Sch...; Niko Beerenwinkel; Christian Beisel; Lara Bernasconi; Anne Bertolini; Bernd Bodenmiller; Ximena Bonilla; Lars Bosshard; Byron Calgua; Ruben Casanova; Stéphane Chevrier; Natalia Chicherova; Ricardo Coelho; Maya D'Costa; Esther Danenberg; Natalie R Davidson; Monica-Andreea Dr...; Reinhard Dummer; Stefanie Engler; Martin Erkens; Katja Eschbach; Cinzia Esposito; André Fedier; Pedro F Ferreira; Joanna Ficek-Pascual; Anja L Frei; Bruno Frey; Sandra Goetze; Linda Grob; Gabriele Gut; Detlef Günther; Pirmin Haeuptle; Viola Heinzelmann...; Sylvia Herter; Rene Holtackers; Tamara Huesser; Alexander Immer; Anja Irmisch; Francis Jacob; Andrea Jacobs; Tim M Jaeger; Katharina Jahn; Alva R James; Philip M Jermann; André Kahles; Abdullah Kahraman; Viktor H Koelzer; Werner Kuebler; Jack Kuipers; Christian P Kunze; Christian Kurzeder; Kjong-Van Lehmann; Mitchell Levesque; Ulrike Lischetti; Flavio C Lombardo; Sebastian Lugert; Gerd Maass; Markus G Manz; Philipp Markolin; Martin Mehnert; Julien Mena; Julian M Metzler; Nicola Miglino; Emanuela S Milani; Holger Moch; Simone Muenst; Riccardo Murri; Charlotte K Y Ng; Stefan Nicolet; Marta Nowak; Monica Nunez Lopez; Patrick G A Pedrioli; Lucas Pelkmans; Salvatore Piscuoglio; Michael Prummer; Prélot Laurie; Natalie Rimmer; Mathilde Ritter; Christian Rommel; María L Rosano-Go...; Gunnar Rätsch; Natascha Santacroce; Jacobo Sarabia de...; Ramona Schlenker; Petra C Schwalie; Severin Schwan; Tobias Schär; Gabriela Senti; Wenguang Shao; Franziska Singer; Sujana Sivapatham; Berend Snijder; Bettina Sobottka; Vipin T Sreedharan; Stefan Stark; Daniel J Stekhoven; Tanmay Tanna; Alexandre P A The...; Tinu M Thomas; Markus Tolnay; Vinko Tosevski; Nora C Toussaint; Mustafa A Tuncel; Marina Tusup; Audrey Van Drogen; Marcus Vetter; Tatjana Vlajnic; Sandra Weber; Walter P Weber; Rebekka Wegmann; Michael Weller; Fabian Wendt; Norbert Wey; Andreas Wicki; Mattheus H E Wild...; Bernd Wollscheid; Shuqing Yu; Johanna Ziegler; Marc Zimmermann; Martin Zoche; Gregor Zuend; Gunnar Rätsch; Kjong-Van Lehmann
• Keywords: Proteomics; 570; 0303 health sciences; Gene Expression Profiling; Computational Biology; Bayes Theorem; 600; Biomedical Informatics; 004; 510; 03 medical and health sciences; Humans; Factor Analysis, Statistical; Melanoma; Algorithms

Abstract

Motivation Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations.

Results We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts.

Availability and implementation The tool is implemented in python and available at https://github.com/ratschlab/path-fa