Method

Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

    • 1School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia;
    • 2Charles Perkins Centre, The University of Sydney, NSW 2006, Australia;
    • 3Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR 999077, China;
    • 4Department of Statistics, Stanford University, Stanford, California 94305-4020, USA;
    • 5Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
    • 6Department of Electrical Engineering, Stanford University, Stanford, California 94305-9505, USA;
    • 7Department of Biomedical Data Science, Stanford University, Stanford, California 94305-5464, USA;
    • 8Bio-X Program, Stanford University, Stanford, California 94305, USA
    • 9 These authors contributed equally to this work.
Published December 15, 2023. https://doi.org/10.1101/gr.277960.123
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cover of Genome Research Vol 36 Issue 6
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June 2026, Vol. 36, No. 6
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Abstract

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space by using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal data sets, we show scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome data set we generated from differentiating mouse embryonic stem cells over time, we show scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

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