PuSH - Publikationsserver des Helmholtz Zentrums München

Inecik, K.* ; Kara, A.* ; Rose, A.* ; Haniffa, M.* ; Theis, F.J.*

TarDis: Achieving robust and structured disentanglement of multiple covariates.

In: (International Conference on Research in Computational Molecular Biology). Berlin [u.a.]: Springer, 2025. 285-289 (Lect. Notes Comput. Sc. ; 15647 LNBI)
Postprint DOI
Open Access Green
Addressing challenges in domain invariance within single-cell genomics necessitates innovative strategies to manage the heterogeneity of multi-source datasets while maintaining the integrity of biological signals. We introduce TarDis, a novel deep generative model designed to disentangle intricate covariate structures across diverse biological datasets, distinguishing technical artifacts from true biological variations. By employing tailored covariate-specific loss components and a self-supervised approach, TarDis effectively generates multiple latent space representations that capture each continuous and categorical target covariate separately, along with unexplained variation. Our extensive evaluations demonstrate that TarDis outperforms existing methods in data integration, covariate disentanglement, and robust out-of-distribution predictions. The model’s capacity to produce interpretable and structured latent spaces, including its pioneering work in ordered latent representations for continuous covariates, markedly enhances its utility in hypothesis-driven research. Consequently, TarDis offers a promising analytical platform for advancing scientific discovery, providing insights into cellular dynamics, and enabling targeted therapeutic interventions (The full paper can be accessed at: https://doi.org/10.1101/2024.06.20.599903).
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Disentanglement ; Generative Models ; Representation Learning ; Self-supervised Learning ; Single-cell Genomics
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Conference on Research in Computational Molecular Biology
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15647 LNBI, Heft: , Seiten: 285-289 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen