Welcome to saSPT’s documentation!
saspt
is a Python tool for analyzing single particle tracking (SPT) experiments. It uses
state arrays, a kind of variational Bayesian framework that learns intelligible models
given raw trajectories from an SPT experiment.
There are a lot of great SPT analysis tools out there. We found it useful to
write saspt
because:
it is simple and flexible enough to accommodate a wide variety of underlying stochastic models;
its outputs are familiar
numpy
andpandas
objects;it imposes no prior beliefs on the number of dynamic states;
it uses tried-and-true Bayesian methods (marginalization over nuisance parameters) to deal with measurement error in a natural way.
I originally wrote saspt
to deal with live cell protein tracking experiments.
In the complex intracellular environment, a protein can occupy a large and unknown number of molecular
states with distinct dynamics. saspt
provides a simple way to measure the number,
characteristics, and fractional occupations of these states. It is also “smart” enough to deal with
situations where there may not be discrete states.
If you want to jump right into working with saspt
, see Quickstart. If you want a
more detailed explanation of why saspt
exists, see Background.
If you want to dig into the guts of the actual model and inference algorithm, see The state array model.
(saspt
stands for “state arrays for single particle tracking”.)
What does saSPT do?
saspt
takes a set of trajectories from a tracking experiment, and identifies a mixture model to explain
them. It is designed to work natively with numpy
and pandas
objects.
What doesn’t saSPT do?
saspt
doesn’t do tracking; it takes trajectories as input. (See: Q. Does saspt provide a way to do tracking?)
saspt
doesn’t model transitions between states. For that purpose, we recommend the excellent vbSPT package.
saspt
doesn’t check the quality of the input data.
saspt
expects you to know the parameters for your imaging experiment, including pixel size, frame rate, and focal depth.
Currently saspt
only supports a small range of physical models. That may change as the package grows.
Contents
- Install
- Quickstart
- Background
- The state array model
- API
- FAQS
- Q. Does
saspt
provide a way to do tracking? - Q. Why doesn’t
saspt
support input format X? - Q. Why are the default diffusion coefficients log-spaced?
- Q. How does
saspt
estimate the posterior occupations, given the posterior distribution? - Q. I want to measure the fraction of particles in a particular state. How do I do that?
- Q. What is defocalization?
- Q. Does
- References