spVIPES.nn.networks.LinearDecoderSPVIPE

spVIPES.nn.networks.LinearDecoderSPVIPE#

class spVIPES.nn.networks.LinearDecoderSPVIPE#

Bases: Module

Linear decoder for spVIPES with shared-private latent space decomposition.

This decoder takes separate shared and private latent representations and decodes them into gene expression parameters. It implements a mixture model that combines shared and private contributions to generate the final output distribution parameters for the negative binomial likelihood.

Parameters:

  • n_input_private (int) – Dimensionality of the private latent space input.

  • n_input_shared (int) – Dimensionality of the shared latent space input.

  • n_output (int) – Number of output features (genes) to reconstruct.

  • n_cat_list (Iterable[int], optional) – List of categorical covariate dimensions for batch correction.

  • use_batch_norm (bool, default False) – Whether to use batch normalization in the decoder layers.

  • use_layer_norm (bool, default False) – Whether to use layer normalization in the decoder layers.

  • bias (bool, default False) – Whether to include bias terms in linear layers.

  • n_hidden (int, default 256) – Number of hidden units in the mixing network.

  • **kwargs – Additional keyword arguments passed to FCLayers.

Notes

The decoder consists of three main components:

  1. Private factor regressor: Maps private latent space to gene-specific factors

  2. Shared factor regressor: Maps shared latent space to gene-specific factors

  3. Mixing network: Learns how to combine shared and private contributions

The output includes both separate private/shared reconstructions and a mixed reconstruction that combines both components according to learned mixing weights.

__init__(n_input_private, n_input_shared, n_output, n_cat_list=None, use_batch_norm=False, use_layer_norm=False, bias=False, n_hidden=256, **kwargs)#
static __new__(cls, *args, **kwargs)#
Return type:

Any

forward(dispersion, z_private, z_shared, library, *cat_list)#

Forward pass through the decoder network.

Parameters:

  • dispersion (str) – Dispersion parameter identifier (currently unused but kept for compatibility).

  • z_private (torch.Tensor) – Private latent representation with shape (batch_size, n_input_private).

  • z_shared (torch.Tensor) – Shared latent representation with shape (batch_size, n_input_shared).

  • library (torch.Tensor) – Library size factors with shape (batch_size, 1) for scaling output rates.

  • *cat_list (int) – Variable length list of categorical covariate indices.

Returns:

tuple Tuple of decoder outputs:

  • px_scale_private : torch.Tensor - Normalized expression rates from private space

  • px_scale_shared : torch.Tensor - Normalized expression rates from shared space

  • px_rate_private : torch.Tensor - Library-scaled rates from private space

  • px_rate_shared : torch.Tensor - Library-scaled rates from shared space

  • px_mixing : torch.Tensor - Learned mixing weights (logits)

  • px_scale : torch.Tensor - Final mixed expression rates

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