Prepare your data

Data organization

Organize your data in a folder SPT_experiment, each sub-folder should contain a file storing the trajectory coordinates in a MDF or CSV file format.

If CSV format is used, the headers should be: x, y, frame, track_id

.
├── data/
│   └── SPT_experiment/
│       ├── Cell_1
│       │    ├── *.tif
│       │    └── *.mdf
│       ├── Cell_2
│       │    ├── *.tif
│       │    └── *.mdf
│       ├── Cell_3
│       │    ├── *.tif
│       │    └── *.mdf
│       └── ...
│
├── src/
├── tracksegnet-env/
├── parms.csv
├── tracksegnet-main.py
└── ...

Change the main parameters

Tune the main parameters of the training in the params.csv file according to your experiment:

• num_states the number of diffusive states for the classification(from 2 to 6 states). This number can vary from 2 to 6 states, but it is recommended to choose 2 to 4 states.

• state_i_diff and state_i_alpha the approximate motion parameters for each of the diffusive state. The diffusion constant is dimensionless, and the anomalous exponent value is ranging from 0 to 2 (: subdiffusion, : Brownian motion, : superdiffusion).

• pt_i_j the probability of transitionning from state i to state j. The total number of probabilities should be .

The remaining parameters are related to the experimental dataset:

• data_path, the path of the dataset of trajectories to segment.

• track_format, the format of the files containing the trajectory coordinates, either MDF (see MTrackJ data file format) or CSV

• time_frame, the time interval between two trajectory points in seconds.

• pixel_size, the dimension of a pixel in .

Note that the program will run on the toy example if the parameters are unchanged.

For updating the parameters of the track simulation and neural network training, please make the changes in the main file tracksegnet-main.py.