scvi.external.decipher.DecipherPyroModule

scvi.external.decipher.DecipherPyroModule#

class scvi.external.decipher.DecipherPyroModule(dim_genes, dim_v=2, dim_z=10, layers_v_to_z=(64,), layers_z_to_x=(), beta=0.1)[source]#

Bases: PyroBaseModuleClass

Pyro Module for the Decipher model.

This module implements the Decipher model for dimensionality reduction and interpretable representation learning in single-cell RNA sequencing data.

Parameters:

dim_genes (int) – Number of genes (features) in the dataset.
dim_v (int (default: 2)) – Dimension of the interpretable latent space v.
dim_z (int (default: 10)) – Dimension of the intermediate latent space z.
layers_v_to_z (Sequence[int] (default: (64,))) – Hidden layer sizes for the v to z decoder network.
layers_z_to_x (Sequence[int] (default: ())) – Hidden layer sizes for the z to x decoder network.
beta (float (default: 0.1)) – Regularization parameter for the KL divergence.

Attributes table#

`device`
`training`

Methods table#

`guide`(x[, beta])
`model`(x[, beta])
`predictive_log_likelihood`(x[, n_samples])	Calculate the predictive log-likelihood for a Decipher module.

Attributes#

DecipherPyroModule.device[source]#

DecipherPyroModule.training: bool#

Methods#

DecipherPyroModule.guide(x, beta=None)[source]#

DecipherPyroModule.model(x, beta=None)[source]#

DecipherPyroModule.predictive_log_likelihood(x, n_samples=5)[source]#

Calculate the predictive log-likelihood for a Decipher module.

This function performs multiple runs through the dataloader to obtain an empirical estimate of the predictive log-likelihood. It calculates the log-likelihood for each run and returns the average. The beta parameter of the Decipher module is temporarily modified and restored even if an exception occurs. Used by default as an early stopping criterion.