The last feature of ExTrack is its position refinement module. It allows the user to get the best spatio-temporal resolution by determining the most likely position (and spread) of the particle at each time point. This will effectively improve the precision of the particle localization when particles are immobile or slowly diffusive without affecting the localization when particles are diffusive.

Like for state predictions, we first need to determine the model parameters. This can be done by running the fitting method or by using parameters previously determined on other data.

Position refinement

When chosing Position refinement, clicking on Next will open the position refinement window:

Number of states and model parameters

The number of states and model parameters can be informed manually or by a prior fitting step.

Frame time (in s)

Provide the time in between frames.

Window length

Provide the window length for which no approximations will be made. Outside the window, similar sequences of states will be merged according to their similarity. A higher window length increases the computation time. Compared to fitting, the window length can be higher as it requires less computation time.

Threshold

Threshold for which similar sequences of states are merged. Increasing the threshold will speedup the method but decrease the quality of the predictions.

Maximum number of sequences

Maximum number of sequences of states to consider. If that number is reached, ExTrack will increase the threshold to keep a number of sequences close to that number. Increasing that number improves the quality of the fit.

Depth of field

Dept of field of the sample. This metric can be important to quantify the probability of tracks to leave the field of view and to therefor avoid the defocalization bias. This parameter assums wide field (or HALO) illumination that allows cytoplasmice tracks to leave from the top or bottom of the depth of field. If you are imaging in TIRF, correct the field of view by multiplying it by 0.7. If you are imaging particles that never leave the depth of field, put a high number (example: 100 * sqrt(2Dtime step)). If you are imaging membrane proteins, you can try to estimate the distance that a particle needs to cross to leave the field of view. If the depth of field is unknown, you can simply put an high value and ignore it. The best way to avoid the defocalization bias is to consider a maximum of track lengths (examples tracks of length 3 to 50).

Save path

Save path for the lifetime histograms of each state. Format: csv. The columns will be POSITION_X, POSITION_Y, FRAME, TRACK_ID, Refined_position_X, Refined_position_Y, Refined_localization_error and the lines will represent the peaks similar to the input format. Optional column names can be given in the first window to add columns to this file.