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<p>Single-cell chromatin accessibility profiles are extremely sparse but reflect continuous developmental trajectories. Most existing methods for dimensionality reduction and trajectory analysis optimize reconstruction error or cluster separation, without encoding temporal continuity in the model or providing metrics tailored to this objective. We introduce iAODE, a variational autoencoder that couples a zero-inflated negative binomial likelihood with a latent Neural ODE, low-weight Kullback–Leibler (KL) regularization, and an interpretable reconstruction bottleneck to learn generative, temporally continuous latent spaces. Around iAODE, we build a standardized AnnData benchmark of 248 single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) and 123 single-cell RNA sequencing (scRNA-seq) datasets and a 20-metric evaluation suite that quantifies latent-space continuity, embedding quality, and clustering-coupling structure. Simulations confirm that the metrics respond smoothly to controlled continuity perturbations, and large-scale benchmarks show that the ODE, low-<i>β</i>, and bottleneck components synergistically improve trajectory structure and robustness over established generative and manifold-learning baselines.</p>

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iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月3日

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  • Zeyu Fu,
  • Chunlin Chen,
  • Song Wang,
  • Junping Wang,
  • Shilei Chen

摘要

Single-cell chromatin accessibility profiles are extremely sparse but reflect continuous developmental trajectories. Most existing methods for dimensionality reduction and trajectory analysis optimize reconstruction error or cluster separation, without encoding temporal continuity in the model or providing metrics tailored to this objective. We introduce iAODE, a variational autoencoder that couples a zero-inflated negative binomial likelihood with a latent Neural ODE, low-weight Kullback–Leibler (KL) regularization, and an interpretable reconstruction bottleneck to learn generative, temporally continuous latent spaces. Around iAODE, we build a standardized AnnData benchmark of 248 single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) and 123 single-cell RNA sequencing (scRNA-seq) datasets and a 20-metric evaluation suite that quantifies latent-space continuity, embedding quality, and clustering-coupling structure. Simulations confirm that the metrics respond smoothly to controlled continuity perturbations, and large-scale benchmarks show that the ODE, low-β, and bottleneck components synergistically improve trajectory structure and robustness over established generative and manifold-learning baselines.