Probabilistic pathway-based multimodal factor analysis
Authors: Immer Alexander , Stark Stefan G , Jacob Francis , Bonilla Ximena , Thomas Tinu , Kahles André , Goetze Sandra , Milani Emanuela S , Wollscheid Bernd ,   The Tumor Profiler Consortium, Aebersold Rudolf , Ak Melike , Al-Quaddoomi Faisal S , Albert Silvana I , Albinus Jonas , Alborelli Ilaria , Andani Sonali , Attinger Per-Olof , Bacac Marina , Baumhoer Daniel , Beck-Schimmer Beatrice , Beerenwinkel Niko , Beisel Christian , Bernasconi Lara , Bertolini Anne , Bodenmiller Bernd , Bonilla Ximena , Bosshard Lars , Calgua Byron , Casanova Ruben , Chevrier Stéphane , Chicherova Natalia , Coelho Ricardo , D'Costa Maya , Danenberg Esther , Davidson Natalie R , Drăgan Monica-Andreea , Dummer Reinhard , Engler Stefanie , Erkens Martin , Eschbach Katja , Esposito Cinzia , Fedier André , Ferreira Pedro F , Ficek-Pascual Joanna , Frei Anja L , Frey Bruno , Goetze Sandra , Grob Linda , Gut Gabriele , Günther Detlef , Haeuptle Pirmin , Heinzelmann-Schwarz Viola , Herter Sylvia , Holtackers Rene , Huesser Tamara , Immer Alexander , Irmisch Anja , Jacob Francis , Jacobs Andrea , Jaeger Tim M , Jahn Katharina , James Alva R , Jermann Philip M , Kahles André , Kahraman Abdullah , Koelzer Viktor H , Kuebler Werner , Kuipers Jack , Kunze Christian P , Kurzeder Christian , Lehmann Kjong-Van , Levesque Mitchell , Lischetti Ulrike , Lombardo Flavio C , Lugert Sebastian , Maass Gerd , Manz Markus G , Markolin Philipp , Mehnert Martin , Mena Julien , Metzler Julian M , Miglino Nicola , Milani Emanuela S , Moch Holger , Muenst Simone , Murri Riccardo , Ng Charlotte K Y , Nicolet Stefan , Nowak Marta , Lopez Monica Nunez , Pedrioli Patrick G A , Pelkmans Lucas , Piscuoglio Salvatore , Prummer Michael , Laurie Prélot , Rimmer Natalie , Ritter Mathilde , Rommel Christian , Rosano-González María L , Rätsch Gunnar , Santacroce Natascha , del Castillo Jacobo Sarabia , Schlenker Ramona , Schwalie Petra C , Schwan Severin , Schär Tobias , Senti Gabriela , Shao Wenguang , Singer Franziska , Sivapatham Sujana , Snijder Berend , Sobottka Bettina , Sreedharan Vipin T , Stark Stefan , Stekhoven Daniel J , Tanna Tanmay , Theocharides Alexandre P A , Thomas Tinu M , Tolnay Markus , Tosevski Vinko , Toussaint Nora C , Tuncel Mustafa A , Tusup Marina , Van Drogen Audrey , Vetter Marcus , Vlajnic Tatjana , Weber Sandra , Weber Walter P , Wegmann Rebekka , Weller Michael , Wendt Fabian , Wey Norbert , Wicki Andreas , Wildschut Mattheus H E , Wollscheid Bernd , Yu Shuqing , Ziegler Johanna , Zimmermann Marc , Zoche Martin , Zuend Gregor , Rätsch Gunnar , Lehmann Kjong-Van 
Published: June 2024

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