sfhmm 0.7.0

step-finding-based HMM

sfHMM

sfHMM (Step-Finding based Hidden Markov Model analysis) provides efficient HMM analysis for varieties of biophysical data. Owing to its accurate parameter estimation workflows, sfHMM can be applied to systems with:

• unknown numbers of states
• high noise
• sparse transition

You can also use sfHMM just for step finding or AIC/BIC based Gaussian mixture model selection. Please refer to "example.ipynb" for basic usages.

Installation

• pip installation

Working in a new environment is highly recommended. For example, if you are using Anaconda:

conda create -n myenv python
conda activate myenv

then it's ready to install sfHMM.

pip install git+https://github.com/hanjinliu/sfHMM

• cloning from the source

If you want to clone from the source, you need Rust installed in your local environment.

git clone https://github.com/hanjinliu/sfHMM
cd sfHMM
pip install -e .

Contents

sfHMM

This module contains basic classes and functions for sfHMM data analysis.

• sfHMM1 ... sfHMM for single trajectory.
• sfHMMn ... sfHMM for multiple trajectories.
• hmm_sampling ... sample data generation of smFRET-like data.
• motor_sampling ... sample data generation of motor-like data.

sfHMM.motor

This module contains sfHMM classes aiming at analyzing motor-stepping or other sparse transition models.

• sfHMM1Motor ... sfHMM for single motor-stepping trajectory.
• sfHMMnMotor ... sfHMM for multiple motor-stepping trajectories.

sfHMM.step

This module contains several step finding algorithms in the same API. My efficient implementation enables analysis of 100,000 data points within ~1 sec!

• GaussStep ... step finding for Gauss distribution [1].
• PoissonStep ... step finding for Poisson distribution.
• SDFixedGaussStep ... step finding for Gauss distribution with fixed standard deviation.
• TtestStep ... step finding using T-test [2].
• BayesianPoissonStep ... step finding in Bayesian method [3].

sfHMM.gmm

This module contains classes that inherit sklearn.mixture.GaussianMixture while making Gaussian mixture model clustering much easier.

• GMMs ... Fitting Gaussian mixtures and model selection.

sfHMM.io

This module contains input/output functions that are suitable for sfHMM data analysis or other one-dimensional signal processing.

• read ... Not recommended now. Use "read" method in sfHMM classes instead
• read_excel ... Load all the sheets from Excel files.
• save ... Not recommended now. Use "save" method in sfHMM classes instead

sfHMM.viewer

This module contains interactive multi-channel viewer for 1-D data visualization (work in progress).

• TrajectoryViewer ... Qt viewer object.

Common Parameters

All the parameters are optional.

• sg0 ... The parameter used in denoising process.
• psf ... The parameter used in step finding.
• krange ... Range of the number of hidden states to test.
• model ... Distribution of signal. Gaussian and Poissonian are supported now.
• name ... Name of the object.

Common Methods

• run_all() ... Run all the needed algorithm in the most plausible condition.
• step_finding() ... Step finding by likelihood maximization.
• denoising() ... The standard deviation of noise is cut off to sg0.
• gmmfit() ... Gaussian mixture model clustering.
• hmmfit() ... HMM parameter initialization and optimization.
• plot() ... Visualize the results of sfHMM analysis.
• tdp() ... Show the results in pseudo transition density plot.
• read() ... Load such as csv, txt, dat files, or the first sheet of Excel files.
• save() ... Save sfHMM analysis results.
• view_in_qt() ... Open a TrajectoryViewer and interactively view the trajectories.

Citation

If you found sfHMM useful, please consider citing our paper.

A fast and objective hidden Markov modeling for accurate analysis of biophysical data with numerous states
Hanjin Liu, Tomohiro Shima
bioRxiv 2021.05.30.446337; doi: https://doi.org/10.1101/2021.05.30.446337

References

[1] Kalafut, B., & Visscher, K. (2008). An objective, model-independent method for detection of non-uniform steps in noisy signals. Computer Physics Communications, 179(10), 716-723.

[2] Shuang, B., Cooper, D., Taylor, J. N., Kisley, L., Chen, J., Wang, W., ... & Landes, C. F. (2014). Fast step transition and state identification (STaSI) for discrete single-molecule data analysis. The journal of physical chemistry letters, 5(18), 3157-3161.

[3] Ensign, D. L., & Pande, V. S. (2010). Bayesian detection of intensity changes in single molecule and molecular dynamics trajectories. Journal of Physical Chemistry B, 114(1), 280–292.