cell_attn.cell_trajectory_data_utils
Classes
Dataset class for cell trajectory data. |
Functions
|
Takes in cell trajectory data in the form of a trajectory matrix, and creates input and label data ready to |
|
Default function to turn a cell trajectory adata matrix into input and label data. Called upon by create_data_from_trajectories(). |
|
Alternative function to turn a trajectory matrix into input and label data when the matrix is large and contains |
Module Contents
- cell_attn.cell_trajectory_data_utils.create_data_from_trajectories(X: numpy.ndarray, nn: int, timestep: int, stdir: str, t_min: int = 0, t_max: int | None = None, memory_saver: bool = False)
Takes in cell trajectory data in the form of a trajectory matrix, and creates input and label data ready to be trained on by a deep attention network.
Parameters
- X: numpy.ndarray
Cell trajectory data matrix (dimension 2 x n_cells x n_timepoints), where X[0,i,t] = x co-ordinate of cell i at time t, and X[1,i,t] = y co-ordinate of cell i at time t.
- nn: int
Number of nearest neighbours.
- timestep: int
Time interval to predict focal cell movement over, measured in indices of the trajectory data matrix.
- stdir: str
Directory to store input and label data in.
- t_min: int | None, default=0
Lower limit of time window of trajectory data to use. If given will filter X down to X[:,:,t_min:] before other computations.
- t_max: int | None, default=None
Upper limit of time window of trajectory data to use. If given will filter X down to X[:,:,:t_max] before other computations.
- memory_saver: bool, default=False
If memory_saver=True, then a more memory efficient (but slower) algorithm will be used. Note this will only use a significantly lower amount of memory if the trajectory data matrix contains a significant amount of missing data.
Creates the following files in stdir:
- input_data.npy: numpy.ndarray
Dimension (n_datapoints x n_nbhs x 12) numpy array. input_data[i] is a 2d numpy array with rows corresponding to the nearest neighbour cells of a focal cell, and the columns are (with {num columns})
nbh_cell_displacement_x _y {2}, focal_cell_velocity_x _y {2}, nbh_cell_velocity_x _y {2}, nbh_cell_displacement_x _y (fcc) {2}.
fcc (focal cell coordinates) indicates that the vector is given in coordinates where the y-axis aligns with the focal cell’s velocity.
- labels.npy: numpy.ndarray:
Dimension (n_datapoints x 2) numpy array, where row i is the next displacement of the focal cell for input_data[i], measured over the given timestep.
- cell_attn.cell_trajectory_data_utils.create_data_from_trajectories_vanilla(X: numpy.ndarray, nn: int, timestep: int, stdir: str)
Default function to turn a cell trajectory adata matrix into input and label data. Called upon by create_data_from_trajectories().
- cell_attn.cell_trajectory_data_utils.create_data_from_trajectories_memory_saver(X: numpy.ndarray, nn: int, timestep: int, stdir: str)
Alternative function to turn a trajectory matrix into input and label data when the matrix is large and contains a large amount of missing data. Called upon by create_data_from_trajectories().
- class cell_attn.cell_trajectory_data_utils.cellTrajectoriesDataset(directory, experiments=None, nsamples_per_run=None, device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
Bases:
torch.utils.data.DatasetDataset class for cell trajectory data.
Parameters
- directory: str
Directory where input and label data is stored, within this directory should be a set of subdirectories, one for each experiment, each containing a input data file called input_data.npy, and label files called labels.npy, as created by create_data_from_trajectories().
- experiments: list[str] | None, default=None
Specify which experiments to take data from for the dataset. Each element of the list should be the name of a subdirectory of directory.
- nsamples_per_run: int | None, default=None
Only include a fixed number of data points per experiment (chosen uniformly at random).
- device: torch.device, default=torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
What device to put the dataset on.
- trajectories
- labels
- device
- __len__()
- __getitem__(i)