PuSH - Publikationsserver des Helmholtz Zentrums München

Litinetskaya, A. ; Schulman, M. ; Curion, F. ; Szalata, A. ; Omidi, A.* ; Lotfollahi, M. ; Theis, F.J.

Integration and querying of multimodal single-cell data with PoE-VAE.

In: (29th RECOMB 2025). Berlin [u.a.]: Springer, 2025. 345-348 (Lect. Notes Comput. Sc. ; 15647 LNBI)
Postprint DOI
Open Access Green
Constructing joint representations from multimodal single-cell datasets is crucial for understanding cellular heterogeneity and function. In this work, we demonstrate the product-of-experts VAE-based model, which offers a flexible, scalable solution for integrating multimodal data, allowing for the seamless mapping of both unimodal and multimodal queries onto a reference atlas. We evaluate how different strategies for combining modalities in the VAE framework impact query-to-reference mapping across diverse datasets, including CITE-seq and spatial metabolomics. We showcase our approach in a mosaic setting, integrating CITE-seq and multiome data to accurately map unimodal and multimodal queries into the joint latent space. We extend this to spatial data by integrating gene expression and metabolomics from paired Visium and MALDI-MSI slides, achieving a high correlation in metabolite predictions from spatial gene expression. Our results demonstrate that this VAE-based framework is scalable, robust, and easily applicable across multiple modalities, providing a powerful tool for data imputation, querying, and biological discovery.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Imputation ; Metabolomics ; Multimodal Integration ; Multimodal Query-to-reference Mapping ; Single-cell ; Spatial Transcriptomics ; Vae
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 29th RECOMB 2025
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15647 LNBI, Heft: , Seiten: 345-348 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of Computational Biology (ICB)