An implementation of the Pathspace Kalman Filter Algorithm as described in C. Agrahar et al. 2024
The Kalman Filter (KF) is an optimal linear state prediction algorithm, with applications in fields as diverse as engineering, economics, robotics, and space exploration. Motivated by an increasing prevalence of biological time-course data, we develop an extension to a non-linear adaptive KF closely related to Bayesian smoothing algorithms, called a Pathspace Kalman Filter (PKF) designed to distinguish between scenarios where a system has not reached steady state and a system in which the underlying dynamics of the biological process is shifting. Specifically, the PKF allows us to a) dynamically track and update the uncertainties associated with the underlying data and prior knowledge, and b) take as input an entire trajectory and an underlying mechanistic model, and using a Bayesian methodology quantify the different sources of uncertainty. An application of this approach is to automatically detect temporal windows where the internal mechanistic model deviates from the data in a time-dependent manner. First, we provide theorems characterizing the convergence of the PKF algorithm. Then, we numerically demonstrate that the PKF outperforms state-of-the-art methods on a synthetic dataset by over an order of magnitude. Finally, we apply this method to biological time-course dataset involving over 1.8 million gene expression measurements.
- Clone the repo
- pip install pathspace-kalman-filter/
Copyright (c) 2025 Altos Labs, Inc.
This software is licensed under the Altos Lab’s Academic Research License (Non-Commercial Use Only). Use is permitted solely for internal, non-commercial research and educational purposes. By using this software, you agree to be bound by the terms of the license. Commercial use is strictly prohibited without a separate license from Altos Labs, Inc. Full license text: https://github.com/altoslabs/pathspace-kalman-filter/blob/main/Altos-Labs-Academic-Research-License-Agreement.docx For commercial licensing inquiries, contact Altos labs.