DeST-OT: Alignment of Spatiotemporal Transcriptomics Data

Peter Halmos, Xinhao Liu, Julian Gold, Feng Chen, Li Ding, Benjamin J. Raphael

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Spatially resolved transcriptomics (SRT) measures mRNA transcripts at thousands of locations within a tissue slice, revealing spatial variations in gene expression as well as the spatial distribution of cell types. In recent studies, SRT has been applied to tissue slices from multiple timepoints during the development of an organism. Alignment of this spatiotemporal transcriptomics data can provide insights into the gene expression programs governing the growth and differentiation of cells over space and time. We introduce DeST-OT (Developmental SpatioTemporal Optimal Transport), a method to align SRT slices from pairs of developmental timepoints using the framework of optimal transport (OT). DeST-OT uses semi-relaxed optimal transport to precisely model cellular growth, death, and differentiation processes that are not well-modeled by existing alignment methods. We further introduce two metrics to quantify the plausibility of a spatiotemporal alignment: a growth distortion metric which quantifies the discrepancy between the inferred and the true cell type growth rates, and a migration metric which quantifies the distance traveled between ancestor and descendant cells.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings
EditorsJian Ma
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages4
ISBN (Print)9781071639887
StatePublished - 2024
Event28th International Conference on Research in Computational Molecular Biology, RECOMB 2024 - Cambridge, United States
Duration: Apr 29 2024May 2 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14758 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th International Conference on Research in Computational Molecular Biology, RECOMB 2024
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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