Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport

Mehmet Yigit Balik, Harri Lähdesmäki
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:5945-5978, 2026.

Abstract

Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a fundamental challenge. Current approaches utilizing neural differential equations and flows are sensitive to overfitting and lack careful considerations of biological variability. In this work, we propose a generative framework that models population trends using a latent heteroscedastic Gaussian process (GP) approximated by Hilbert space methods. To address the absence of genuine cell trajectories, we leverage an optimal transport (OT) objective that aligns generated and observed population distributions. Our method explicitly captures biological heterogeneity by incorporating cell-specific latent time and cell type conditioning to disentangle temporal asynchrony and trajectories to different cell types. We demonstrate state-of-the-art performance on complex interpolation and extrapolation benchmarks and introduce a novel gradient-based strategy for inferring perturbation trajectories.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-balik26a, title = {Modeling Temporal sc{RNA}-seq Data with Latent {G}aussian Process and Optimal Transport}, author = {Balik, Mehmet Yigit and L\"{a}hdesm\"{a}ki, Harri}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {5945--5978}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/balik26a/balik26a.pdf}, url = {https://proceedings.mlr.press/v306/balik26a.html}, abstract = {Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a fundamental challenge. Current approaches utilizing neural differential equations and flows are sensitive to overfitting and lack careful considerations of biological variability. In this work, we propose a generative framework that models population trends using a latent heteroscedastic Gaussian process (GP) approximated by Hilbert space methods. To address the absence of genuine cell trajectories, we leverage an optimal transport (OT) objective that aligns generated and observed population distributions. Our method explicitly captures biological heterogeneity by incorporating cell-specific latent time and cell type conditioning to disentangle temporal asynchrony and trajectories to different cell types. We demonstrate state-of-the-art performance on complex interpolation and extrapolation benchmarks and introduce a novel gradient-based strategy for inferring perturbation trajectories.} }
Endnote
%0 Conference Paper %T Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport %A Mehmet Yigit Balik %A Harri Lähdesmäki %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-balik26a %I PMLR %P 5945--5978 %U https://proceedings.mlr.press/v306/balik26a.html %V 306 %X Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a fundamental challenge. Current approaches utilizing neural differential equations and flows are sensitive to overfitting and lack careful considerations of biological variability. In this work, we propose a generative framework that models population trends using a latent heteroscedastic Gaussian process (GP) approximated by Hilbert space methods. To address the absence of genuine cell trajectories, we leverage an optimal transport (OT) objective that aligns generated and observed population distributions. Our method explicitly captures biological heterogeneity by incorporating cell-specific latent time and cell type conditioning to disentangle temporal asynchrony and trajectories to different cell types. We demonstrate state-of-the-art performance on complex interpolation and extrapolation benchmarks and introduce a novel gradient-based strategy for inferring perturbation trajectories.
APA
Balik, M.Y. & Lähdesmäki, H.. (2026). Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:5945-5978 Available from https://proceedings.mlr.press/v306/balik26a.html.

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