Spatial Autocorrelation Predicts Cross-Modal Learnability: A Systematic Benchmark of Metabolite Prediction from Gene Expression

Maiia Shulman, Anastasia Litinetskaya, Eirini Giannakoulia, Fabian J Theis

Confirmation: I have read and agree with the workshop's policy on behalf of myself and my co-authors.

Track: long paper (4–8 pages excluding references)

Keywords: Spatial omics, multi-modal integration, cross-modal learning, metabolomics

TL;DR: We show that spatial autocorrelation predicts cross-modal learnability, explaining when metabolomics can and cannot be inferred from transcriptomics.

Abstract:

Understanding which molecular states can be learned across measurement modal- ities is fundamental to multi-omics integration. We systematically evaluate whether metabolite abundances can be predicted from gene expression using the first spatially-matched transcriptomics-metabolomics dataset. Here we present the first comprehensive benchmark of transcriptome-to-metabolome
prediction in spatial multi-omics. Our systematic evaluation spans seven architectures, from regularized linear models to graph neural networks, across multiple feature selection strategies and validation protocols, implemented via a reproducible Snakemake pipeline. Moving beyond aggregate performance metrics, we characterize 500 individual metabolites to identify the distributional properties that determine learnability. This analysis reveals a three-tier biological hierarchy linking spatial organization to prediction success and post-translational regulation to fundamental limits, providing quantitative criteria for when computational inference can substitute for experimental measurement.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Maiia_Shulman1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Submission Number: 92