用流模型统一追踪密度比，加速基因组数据对比分析。
Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics
- 构建动态方程统一建模密度比变化轨迹。
- 在模拟数据上表现媲美现有方法，计算更高效。
- 适用于单细胞基因组学中的药物效应与批次校正评估。
估计不可解析分布对之间的密度比是概率建模的核心问题，支持不同实验条件下样本似然的合理比较。尽管精确似然模型如归一化流为密度比估计提供了有前景的路径，但直接评估存在计算开销大且易受离散化误差影响的问题，因需独立模拟每个分布的似然。本文利用条件感知流匹配，推导出沿生成轨迹追踪密度比的单一动力学公式。我们在闭式解基准测试中展示了具有竞争力的性能，并证明该方法可支持单细胞基因组数据分析中的多样化任务，使基于似然的细胞状态比较成为可能，从而实现治疗效果估计和批次校正评估。
原文摘要 · Abstract (English)
Estimating density ratios between pairs of intractable data distributions is a core problem in probabilistic modeling, enabling principled comparisons of sample likelihoods under different data-generating processes across conditions. While exact-likelihood models such as normalizing flows offer a promising approach to density ratio estimation, naive evaluations are computationally expensive and prone to discretization errors because they require simulating each distribution's likelihood independently. In this work, we leverage condition-aware flow matching to derive a single dynamical formulation for tracking density ratios along generative trajectories. We demonstrate competitive performance on simulated benchmarks for closed-form ratio estimation, and show that our method supports versatile tasks in single-cell genomics data analysis, where likelihood-based comparisons of cellular states across experimental conditions enable treatment effect estimation and batch correction evaluation.
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