Heteromotility is a tool for analyzing cell motility in a quantitative manner. Heteromotility takes cell tracking data as input and calculates 70+ motility features that can be used to generate a "motility fingerprint" for a given cell. By analyzing more features of cell motility than most common cell tracking methods, Heteromotility may be able to identify novel heterogenous motility phenotypes.
Heteromotility also contains a suite of tools to quantify and visualize cell state spaces, and dynamic state transitions within the state space. While these tools were developed for use with Heteromotility features, they may be applied to any arbitrary time-series feature set.
Heteromotility is developed by Jacob Kimmel in the Laboratory of Cell Geometry in the Center for Cellular Contstruction at the University of California, San Francisco.
We have a recent paper in PLoS Computational Biology using Heteromotility analysis to quantify dynamic cell state transitions in muscle stem cells and a cancer cell model. Check it out and let us know what you think!
We also have videos of Heteromotility analysis in action in multiple cell types on our lab website's Heteromotility page.
For tutorials and complete API documentation, please see Heteromotility on Read the Docs.
Heteromotility depends on >=python3.6. The tool will operate using >=python2.7, but this is not supported. We recommend the Anaconda Python distribution.
Heteromotility can be installed from the Python Package Index with pip
$ pip install heteromotility
or
Simply clone this repository and run setup.py in the standard manner.
$ git clone https://github.com/cellgeometry/heteromotility
$ cd heteromotility
$ python setup.py install
$ heteromotility -h
usage: Calculate motility features from cell locations or cell paths
[-h] [--seg SEG] [--exttrack EXTTRACK] input_dir output_dir
...
Both methods of package installation will add an alias heteromotility to your PATH environment variable.
As with all research software, we recommend installing Heteromotility inside of a virtual environment.
$ virtualenv heteromotility_env/
$ source heteromotility_env/bin/activate
$ pip install heteromotility
| Feature Name | Notation | Description |
|---|---|---|
| Total Distance | total_distance | total distance traveled |
| Net Distance | net_distance | net distance traveled |
| Minimum Speed | min_speed | minimum overall speed |
| Maximum Speed | max_speed | maximum speed |
| Average Speed | avg_speed | average overall speed |
| Time Spent Moving [1,10] | time_moving | proportion of time in motion, variable time intervals considered [1,10] frames |
| Average Moving Speed [1,10] | avg_moving_speed | average speed during movement, variable time intervals considered [1,10] frames |
| Linearity | linearity | Pearson's r2 of regression through all cell positions |
| Spearman's rho2 | spearmanrsq | Spearman's rho2 through all cell positions |
| Progressivity | progressivity | (net distance traveled / total distance traveled) |
| Mean Square Displacement Coefficient | msd_alpha | coefficient of log(tau) in a plot of log(tau) vs log(MSD) |
| Random Walk Linearity Comparison | rw_linearity | (cell path linearity - simulated random walk linearity) |
| Random Walk Net Distance Comparison | rw_net_distance | (cell path net distance - simulated random walk net distance) |
| Random Walk Kurtosis Comparison [1,10] | diff_kurtosis | (cell displacement kurtosis - random walk kurtosis) for variable time intervals |
| Hurst Exponent (Rescaled-Range) | hurst_RS | Hurst exponent estimation using Mandelbrot's rescaled range methods |
| Autocorrelation [1,10] | autocorr | Autocorrelation of the displacement distribution for variable time lags |
| Non-Gaussian coefficient | nongauss | Non-Gaussian coefficient (alpha_2) of the displacement distribution |
| Proportion of Right Turns [time_lag, estimation_interval] | p_turn | proportion of turns an object makes to the left, calculated for multiple time lags and direction estimation intervals |
| Minimum Turn Magnitude | min_theta | minimum turn angle in radians, for various time lags and direction estimation intervals |
| Maximum Turn Magnitude | max_theta | maximum turn angle in radians, for various time lags and direction estimation intervals |
| Average Turn Magnitude | mean_theta | mean turn angle in radians, for various time lags and direction estimation intervals |
Heteromotility is invoked as a command line tool from your terminal of choice. For the following examples, it is assumed that the alias heteromotility has been added to your PATH environment variable, per the installation methods above.
Commands will differ slightly if you are invoking the module directly. i.e.) heteromotility will be /path/to/heteromotility.py.
The general method to calculate motility statistics is:
Pickled Cell Paths
$ heteromotility output_path/ --exttrack path/to/object_paths.pickle
Tracks X and Y in CSVs
$ heteromotility output_path/ --tracksX path/to/tracksX.csv --tracksY path/to/tracksY.csv
Both methods will output a CSV named motility_statistics.csv in the specified output directory, formatted with features as columns and samples as rows.
The optional argument --output_suffix can be used to add a suffix to the output CSV.
$ heteromotility /path/to/output_dir
$ ls
motility_statistics.csv
$ heteromotility /path/to/output_dir --output_suffix TEST_SUFFIX
$ ls /path/to/output_dir
motility_statistics.csv motility_statistics_TEST_SUFFIX.csv
As a demonstration, simulated random walk and Levy flier cell paths are provided in the demo/ directory.
To calculate Heteromotility features for a simulated path, simply run the following.
$ heteromotility demo/ --tracksX demo/rw_x.csv --tracksY demo/rw_y.csv --output_suffix rw
$ heteromotility demo/ --tracksX demo/pf_x.csv --tracksY demo/pf_y.csv --output_suffix pf
Heteromotility supports splitting an object's path into multiple subpaths and calculating features for each subpath. This is useful to investigate changes in an object's motility over time. This feature is triggered with the --detailedbalance command line flag, followed by an integer specifying the minimum number of path steps to consider. Heteromotility will calculate features for every subpath of the minimum length, and every possible length up to 1/2 the total length of the supplied path.
Note: Subpaths have a minimum length of 20 steps, as multiple Heteromotility features rely upon regression analyses that are confounded by exceedingly small path lengths.
This feature is executed like so:
$ heteromotility /output/path/ --tracksX /path/to/tracksX.csv --tracksY /path/to/tracksY.csv --detailedbalance 20
where the integer 20 can be replaced by your desired minimum path length.
The resulting output files will be named motility_statistics_split_LENGTH.csv where LENGTH is the subpath size for those features. Importantly, the cell_ids column will now contain additional information. In addition to the unique cell identifier, cell_ids will contain a dash - following the identifier with subpath number, indexed beginning at 0. The format appears:
cell_ids ...
ObjectIdentifier-SubpathNumber ...
...
For instance, for a path with total length N = 80, minimum length tau = 20, the cell_ids column would appear as follows:
cell_ids ...
obj0-0 ...
obj0-1 ...
obj0-2 ...
obj0-3 ...
obj1-1 ...
obj1-2 ...
...
Where obj0-0 is the first subpath (length = 20) of obj0, obj0-1 is the second subpath of obj0, and so forth.
Heteromotility accepts object paths as N x T CSVs, where N is the number of samples and T is the number of time steps. One CSV encodes an object's position in the X dimension, and another in the Y dimension.
Each row describes a single sample, and each column contains the objects position in the X or Y dimension at the corresponding time step, ordered 0 to T, left to right.
For instance, tracksX.csv may contain the following:
0, 5, 4, 5, 7, 5, 10, ...
38, 51, 42, 38, 41, 43, ...
...
and likewise for tracksY.csv.
Heteromotility also accepts a pickled Python dictionary of object paths as an input. The dictionary should be keyed by a unique object identifier (i.e. numbers, names), with each key corresponding to a sequential list of XY-point tuples.
object_paths = {
obj1 : [(x1,y1), (x2,y2), (x3,y3)...],
obj2 : [(x1,y1), (x2,y2), (x3,y3)...],
...
}
To export this dictionary as a pickle, use the standard Python pickle library.
> import pickle
> with file('output_name.pickle', 'wb') as of:
> pickle.dump(object_paths, of)