MoCoO: Momentum Contrast ODE-Regularized VAE for Single-Cell Trajectory Inference and Representation Learning

Authors

Fu, Z.

Abstract

Characterising cellular differentiation from single-cell RNA sequencing (scRNA-seq) requires representations that capture both discrete cell-type identity and continuous developmental trajectories. We present MoCoO, a modular framework integrating a Variational Autoencoder (VAE), Neural Ordinary Differential Equations (Neural ODE), and Momentum Contrast (MoCo), complemented by a systematic Phase-2 Flow Matching (FM) refinement step applicable to all model variants. Through a systematic six-configuration ablation across 20 scRNA-seq datasets evaluated with a proposed five-metric suite covering clustering geometry (ASW, DAV, CAL) and embedding quality (DRE, DREX), we demonstrate two central findings. First, the ODE+MoCo combination is the core architectural synergy: VAE+ODE+MoCo achieves four of five top-two finishes among base configurations, including the best ASW (0.225) and DAV (1.478), plus second-best DRE (0.640) and CAL. Second, FM refinement systematically improves both embedding quality and clustering geometry across all six base configurations---DREX in 92% and DRE in 88% of 120 dataset--configuration pairs ({Delta}DREX=+0.030, {Delta}DRE=+0.023), CAL in 88%, ASW in 86% ({Delta}ASW=+0.018), and DAV in 80% ({Delta}DAV=-0.072; Fig. 2). Combined, the full MoCoO pipeline (VAE+ODE+MoCo+Proto+FM) achieves the best DRE (0.678), DREX (0.660), and CAL, while VAE+ODE+MoCo+FM achieves the best ASW (0.257) and DAV (1.359). ODE smooths the latent manifold along developmental trajectories; MoCo sharpens cluster geometry; FM recovers and amplifies both embedding quality and cluster separation post-hoc. Downstream validation confirms that MoCoO latent spaces support annotation transfer, uncertainty quantification, differential expression, and branching detection. Pseudotime predictions correlate significantly with canonical marker genes across all five core developmental systems. We publicly release the MoCoO Python package (pip install mocoo) and full benchmark suite.

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The authors list and abstract were imported from bioRxiv on 01 Apr 2026.

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