Pseudotime-conditioned diffusion models for imputing time-series single-cell data

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ID: 326294
2026
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Abstract
Abstract Motivation Probabilistic diffusion-based imputation methods have demonstrated superior performance over many state-of-the-art approaches for imputing single-cell data. However, existing methods typically rely on isotropic Gaussian noise and canonical observation time, limiting their ability to capture structured temporal dependencies and continuous cellular dynamics. In time-series single-cell RNA-seq (scRNA-seq) data, these limitations are exacerbated by the small number of experimentally measured time points and the substantial heterogeneity among cells collected at the same time point, causing observed time labels to poorly reflect true biological progression. Results To address these challenges, we propose the first pseudotime-conditioned diffusion-based imputation framework for time-series scRNA-seq data, consisting of two trajectory-aware models: pseudotime-conditioned conditional score-based diffusion imputation (P-CSDI) and pseudotime-conditioned hybrid-noise CSDI (PH-CSDI). P-CSDI incorporates inferred pseudotime as a conditioning signal to guide the denoising process with biologically meaningful temporal structure, while PH-CSDI further introduces hybrid structured noise to improve frequency-aware temporal modeling. By leveraging pseudotime as a continuous representation of cellular progression, both models enable trajectory-aware and temporally coherent reconstruction of missing gene expression values. P-CSDI and PH-CSDI consistently reduce mean absolute error (MAE) and root mean squared error (RMSE) by approximately 40%–60% compared with state-of-the-art imputation methods across two scRNA-seq datasets under diverse missingness settings. These findings highlight the importance of incorporating biologically informed temporal structure into diffusion-based generative models for accurate imputation of dynamic single-cell transcriptomic data. Availability The computer code and data for the proposed method are available on GitHub: https://github.com/gbishop345/pseudotime-CSDI.
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openalex_W7204116376 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Graham Bishop, Min-Ho Ku, Daniel Lee, Haijun Gong, Tong Si
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag248
URL
https://doi.org/10.1093/bioadv/vbag248
Keywords Keywords not found

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