sctour.train.Trainer

class sctour.train.Trainer(adata: anndata._core.anndata.AnnData, percent: Optional[float] = None, n_latent: int = 5, n_ode_hidden: int = 25, n_vae_hidden: int = 128, batch_norm: bool = False, ode_method: str = 'euler', step_size: Optional[int] = None, alpha_recon_lec: float = 0.5, alpha_recon_lode: float = 0.5, alpha_kl: float = 1.0, loss_mode: Literal['mse', 'nb', 'zinb'] = 'nb', nepoch: Optional[int] = None, batch_size: int = 1024, drop_last: bool = False, lr: float = 0.001, wt_decay: float = 1e-06, eps: float = 0.01, random_state: int = 0, val_frac: float = 0.1, use_gpu: bool = True)[source]

Class for implementing the scTour training process.

Parameters

  • adata – An AnnData object for the training data.

  • percent – The percentage of cells used for model training. Default to 0.2 when the cell number > 10,000 and to 0.9 otherwise.

  • n_latent – The dimensionality of the latent space. (Default: 5)

  • n_ode_hidden – The dimensionality of the hidden layer for the latent ODE function. (Default: 25)

  • n_vae_hidden – The dimensionality of the hidden layer for the VAE. (Default: 128)

  • batch_norm – Whether to include a BatchNorm layer. (Default: False)

  • ode_method – The solver for ODE. List of ODE solvers can be found in torchdiffeq. (Default: ‘euler’)

  • step_size – The step size during integration.

  • alpha_recon_lec – The scaling factor for the reconstruction error from encoder-derived latent space. (Default: 0.5)

  • alpha_recon_lode – The scaling factor for the reconstruction error from ODE-solver-derived latent space. (Default: 0.5)

  • alpha_kl – The scaling factor for the KL divergence in the loss function. (Default: 1.0)

  • loss_mode – The mode for calculating the reconstruction error. (Default: ‘nb’) Three modes are included: 'mse': mean squared error; 'nb': negative binomial conditioned likelihood; 'zinb': zero-inflated negative binomial conditioned likelihood.

  • nepoch – Number of epochs.

  • batch_size – The batch size during training. (Default: 1024)

  • drop_last – Whether or not drop the last batch when its size is smaller than batch_size. (Default: False)

  • lr – The learning rate. (Default: 1e-3)

  • wt_decay – The weight decay (L2 penalty) for Adam optimizer. (Default: 1e-6)

  • eps – The eps parameter for Adam optimizer. (Default: 0.01)

  • random_state – The seed for generating random numbers. (Default: 0)

  • val_frac – The percentage of data used for validation. (Default: 0.1)

  • use_gpu – Whether to use GPU when available. (Default: True)

Methods

get_latentsp([alpha_z, alpha_predz, ...])

Infer the latent space.

get_time()

Infer the developmental pseudotime.

get_vector_field(T, Z)

Infer the vector field.

save_model(save_dir, save_prefix)

Save the trained scTour model.

train()

Model training.