scvi.module.AmortizedLDAPyroModule

class scvi.module.AmortizedLDAPyroModule(n_input, n_topics, n_hidden, cell_topic_prior=None, topic_feature_prior=None)[source]

Bases: scvi.module.base._base_module.PyroBaseModuleClass

An amortized implementation of Latent Dirichlet Allocation [Blei03] implemented in Pyro.

This module uses auto encoding variational Bayes to optimize the latent variables in the model. In particular, a fully-connected neural network is used as an encoder, which takes in feature counts as input and outputs the parameters of cell topic distribution. To employ the reparametrization trick stably, the Dirichlet priors are approximated by a Logistic-Normal distribution. The input feature counts tensor is a cell by features Bag-of-Words(BoW) representation of the counts. I.e. the model treats each cell’s feature vector as ordered, not as unordered as in a Multinomial distribution.

Parameters
n_input : intint

Number of input features.

n_topics : intint

Number of topics/topics to model.

n_hidden : intint

Number of nodes in the hidden layer of the encoder.

cell_topic_prior : float | Sequence[float] | NoneUnion[float, Sequence[float], None] (default: None)

Prior of cell topic distribution. If None, defaults to 1 / n_topics.

topic_feature_prior : float | Sequence[float] | NoneUnion[float, Sequence[float], None] (default: None)

Prior of topic feature distribution. If None, defaults to 1 / n_topics.

Attributes

guide

model

Methods

get_elbo(x, library, n_obs)

Computes ELBO.

get_topic_distribution(x, n_samples)

Converts x to its inferred topic distribution.

topic_by_feature(n_samples)

Gets a Monte-Carlo estimate of the expectation of the topic by feature matrix.