PRIME: scalable, robust inference of mechanistic cell states from multimodal single-cell counts via probability generating functions
Abstract
Single-cell multiomic technologies can now quantify complementary RNA species within the same cell, creating an opportunity to move beyond descriptive clustering toward mechanistically interpretable cell states. Yet most current methods depend on heuristic integration steps and become computationally burdensome at scale, limiting their ability to robustly detect subtle kinetic differences across heterogeneous populations. Here we introduce PRIME, a scalable framework for mechanistic cell-state discovery from multimodal single-cell count data. PRIME embeds multimodal measurements in a probability generating function (PGF) space, where transcriptional dynamics are encoded compactly and compared efficiently. This representation enables robust inference of latent kinetic structure and supports rapid cell grouping with a power K-means backbone that remains stable under noise, sparsity, and multimodality. Across synthetic benchmarks and experimental multimodal datasets, PRIME consistently recovers cell populations distinguished by transcriptional kinetics, outperforms conventional integration-and-clustering pipelines in robustness, and yields interpretable parameters that link observed variability to underlying regulatory mechanisms. By providing a mathematically principled yet practical route from multimodal counts to kinetic cell states, PRIME empowers biologists to uncover dynamic transcriptional regimes, dissect regulatory heterogeneity, and connect cell identity to mechanism rather than markers.
Full-text reasoning
From the deep-tier full-text analysis of this preprint.
Reasoning review — 6 key claims, 3 well-supported, 3 with gaps
- supported PRIME consistently recovers cell populations distinguished by transcriptional kinetics across diverse datasets.
- supported PRIME demonstrates superior robustness compared to conventional integration-and-clustering pipelines.
- supported PRIME provides interpretable kinetic parameters linking observed variability to underlying regulatory mechanisms.
- partial PRIME's PGF space representation enables robust inference of latent kinetic structure. gap: The text describes the theoretical benefits of PGFs for robustness but does not provide direct evidence or comparisons demonstrating that the PGF representation itself (separate from the power K-means backbone) is robust to noise, sparsity…
- partial PRIME's power K-means backbone ensures stable optimization for identifying reproducible kinetic states. gap: While the rationale for power K-means is strong and indirect evidence exists, the manuscript does not directly compare PRIME's performance with and without the power K-means backbone to demonstrate its specific contribution to stable optim…
- partial PRIME offers a mathematically principled and practical route for mechanistic cell-state discovery. gap: The manuscript describes the mathematical principles but does not provide direct evidence of PRIME's 'practicality' in terms of computational efficiency or ease of use, nor does it demonstrate its scalability.
Claims and gaps are read from the full text by a language model, shown for transparency; they do not affect ranking or selection.
Lifecycle
- biorxiv v1 2026-06-09 source ↗
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