[论文解读] An extension to reversible jump Markov chain Monte Carlo for change point problems with heterogeneous temporal dynamics

Emily M. Gribbin, Benjamin Davis+2 more|arXiv (Cornell University)|Feb 19, 2026

Advanced Fluorescence Microscopy Techniques被引用 0

一句话总结

该论文将可组合变点移动的 RJMCMC 扩展为 CRJMCMC，以检测时间序列中的短暂状态变化，应用于 FLImP 光漂白步分析，在不需要标定的情况下提高每帧荧光团计数的准确性。

ABSTRACT

Detecting brief changes in time-series data remains a major challenge in fields where short-lived states carry meaning. In single-molecule localisation microscopy, this problem is particularly acute as fluorescent molecules used to tag protein oligomers display heterogenous photophysical behaviour that can complicate photobleach step analysis; a key step in resolving nanoscale protein organisation. Existing methods often require extensive filtering or prior calibration, and can fail to accurately account for blinking or reversible dark states that may contaminate downstream analysis. In this paper, an extension to RJMCMC is proposed for change point detection with heterogeneous temporal dynamics. This approach is applied to the problem of estimating per-frame active fluorophore counts from one-dimensional integrated intensity traces derived from Fluorescence Localisation Imaging with Photobleaching (FLImP), where compound change point pair moves are introduced to better account for short-lived events known as blinking and dark states. The approach is validated using simulated and experimental data, demonstrating improved accuracy and robustness when compared with current photobleach step analysis methods and with the existing analysis approach for FLImP data. This Compound RJMCMC (CRJMCMC) algorithm performs reliably across a wide range of fluorophore counts and signal-to-noise conditions, with signal-to-noise ratio (SNR) down to 0.001 and counts as high as nineteen fluorophores, while also effectively estimating low counts observed when studying EGFR oligomerisation. Beyond single molecule imaging, this work has applications for a variety of time series change point detection problems with heterogeneous state persistence. For example, electrocorticography brain-state segmentation, fault detection in industrial process monitoring and realised volatility in financial time series.

研究动机与目标

• 解决在具有异质动态的时间序列中检测简短且有意义的状态变化的挑战。
• 开发可扩展的 RJMCMC 扩展，能够同时添加/删除接近的变点以建模短暂事件。
• 将该方法应用于 FLImP 的一维积分强度轨迹，以估计每帧的活跃荧光团计数。
• 在光漂白步分析中展示对先验标定和用户输入的依赖减少。

提出的方法

• 引入具有 k 个变点的多变点模型，且 k 服从泊松分布。
• 对帧强度使用高斯噪声，均值 mu_i = mu_f n_i + mu_b，方差 sigma_i^2 = sigma_f^2 n_i + sigma_b^2，其中 n_i 为活跃荧光团数。
• 提出复合出生-死亡移动，增加或移除成对的密集变点以捕捉短暂的闪烁/暗态。
• 在 RJMCMC 结构中保留出生、死亡与位移移动，但对移动进行定制以适应整数活跃荧光团计数。
• 用先验初始化并更新强度参数 mu_f、mu_b、sigma_f^2、sigma_b^2，以避免需要大量标定。
• 在模拟和实验 FLImP 轨迹上进行验证，比较 RMSE 与最先进方法及 FLImP 的现有分析结果。

实验结果

研究问题

• RQ1 CRJMCMC 是否能够在没有先前标定的情况下可靠地检测出短暂的闪烁和暗态？
• RQ2 在不同荧光团数量、信噪比和状态转换频率下，CRJMCMC 在估计每帧活跃荧光团计数方面的表现如何？
• RQ3 与基于 MAP 的方法和 HMM-MCMC 方法相比，CRJMCMC 在FLImP 数据的准确性和鲁棒性方面表现如何？

主要发现

• CRJMCMC 在最多十七个荧光团时保持低 RMSE，并且在信噪比降至 0.001 时仍具鲁棒性。
• CRJMCMC 的表现优于单调 MAP 和序列 MAP，在低荧光团计数时与因子化 HMM-MCMC 相竞争。
• CRJMCMC 能处理短暂状态，不需要大量过滤或带标签的标定，使 FLImP DNA origami 数据可用帧数增加约 30%。
• CRJMCMC 的平均运行时间为每条轨迹 89.31 秒，快于因子化 HMM-MCMC（324.81 秒），慢于 MAP 方法，且 CPU 并行化显示进一步提速潜力。
• 在实验性 FLImP 轨迹上，与 FLImP 基准轨迹相比，CRJMCMC 在两、三、四荧光团水平下均实现高帧精度、准确性和灵敏度。