scvi.module.SCANVAE#
- class scvi.module.SCANVAE(n_input, n_batch=0, n_labels=0, n_hidden=128, n_latent=10, n_layers=1, n_continuous_cov=0, n_cats_per_cov=None, dropout_rate=0.1, dispersion='gene', log_variational=True, gene_likelihood='zinb', y_prior=None, labels_groups=None, use_labels_groups=False, classifier_parameters=None, use_batch_norm='both', use_layer_norm='none', **vae_kwargs)[source]#
Bases:
VAE
Single-cell annotation using variational inference.
This is an implementation of the scANVI model described in [Xu et al., 2021], inspired from M1 + M2 model, as described in (https://arxiv.org/pdf/1406.5298.pdf).
- Parameters:
n_input (int) – Number of input genes
n_batch (int) – Number of batches
n_labels (int) – Number of labels
n_hidden (Tunable_[int]) – Number of nodes per hidden layer
n_latent (Tunable_[int]) – Dimensionality of the latent space
n_layers (Tunable_[int]) – Number of hidden layers used for encoder and decoder NNs
n_continuous_cov (int) – Number of continuous covarites
n_cats_per_cov (Optional[Iterable[int]]) – Number of categories for each extra categorical covariate
dropout_rate (Tunable_[float]) – Dropout rate for neural networks
dispersion (Tunable_[Literal['gene', 'gene-batch', 'gene-label', 'gene-cell']]) –
One of the following
'gene'
- dispersion parameter of NB is constant per gene across cells'gene-batch'
- dispersion can differ between different batches'gene-label'
- dispersion can differ between different labels'gene-cell'
- dispersion can differ for every gene in every cell
log_variational (Tunable_[bool]) – Log(data+1) prior to encoding for numerical stability. Not normalization.
gene_likelihood (Tunable_[Literal['zinb', 'nb']]) –
One of:
'nb'
- Negative binomial distribution'zinb'
- Zero-inflated negative binomial distribution
y_prior – If None, initialized to uniform probability over cell types
use_labels_groups (bool) – Whether to use the label groups
use_batch_norm (Tunable_[Literal['encoder', 'decoder', 'none', 'both']]) – Whether to use batch norm in layers
use_layer_norm (Tunable_[Literal['encoder', 'decoder', 'none', 'both']]) – Whether to use layer norm in layers
**vae_kwargs – Keyword args for
VAE
Attributes table#
Methods table#
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Classify cells into cell types. |
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Compute the loss. |
Attributes#
training
Methods#
classification_loss
classify
- SCANVAE.classify(x, batch_index=None, cont_covs=None, cat_covs=None)[source]#
Classify cells into cell types.
loss