To perform 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.

State predictions

When chosing state labeling, clicking on Next will open the labeling 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).

Plot labeled tracks

Plot a random set of tracks labeled with their state probabilities. If two states, red corespond to tracks identified to be in the least diffusive state with a probability of 100% while blue correspond to tracks identified in the most diffusive state with a probability of 100%. If three or more states the colors of the different states are distributed in a rgb colormap. Intermediate colors represent intermediate probabilities.

Save path

Save path for the tracks labeled with their state predictions. Format: csv with lines that represent individual peaks.