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', use_observed_lib_size=True, y_prior=None, labels_groups=None, use_labels_groups=False, linear_classifier=False, classifier_parameters=None, use_batch_norm='both', use_layer_norm='none', **vae_kwargs)[source]#
Bases:
SupervisedModuleClass
,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 genesn_batch (
int
(default:0
)) – Number of batchesn_labels (
int
(default:0
)) – Number of labelsn_hidden (
int
(default:128
)) – Number of nodes per hidden layern_latent (
int
(default:10
)) – Dimensionality of the latent spacen_layers (
int
(default:1
)) – Number of hidden layers used for encoder and decoder NNsn_continuous_cov (
int
(default:0
)) – Number of continuous covaritesn_cats_per_cov (
Iterable
[int
] |None
(default:None
)) – Number of categories for each extra categorical covariatedropout_rate (
float
(default:0.1
)) – Dropout rate for neural networksdispersion (
Literal
['gene'
,'gene-batch'
,'gene-label'
,'gene-cell'
] (default:'gene'
)) –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 (
bool
(default:True
)) – Log(data+1) prior to encoding for numerical stability. Not normalization.gene_likelihood (
Literal
['zinb'
,'nb'
] (default:'zinb'
)) –One of:
'nb'
- Negative binomial distribution'zinb'
- Zero-inflated negative binomial distribution
use_observed_lib_size (
bool
(default:True
)) – IfTrue
, use the observed library size for RNA as the scaling factor in the mean of the conditional distribution.y_prior (
Tensor
|None
(default:None
)) – If None, initialized to uniform probability over cell typeslabels_groups (
Sequence
[int
] (default:None
)) – Label group designationsuse_labels_groups (
bool
(default:False
)) – Whether to use the label groupslinear_classifier (
bool
(default:False
)) – If True, uses a single linear layer for classification instead of a multi-layer perceptron.classifier_parameters (
dict
|None
(default:None
)) – Keyword arguments passed intoClassifier
.use_batch_norm (
Literal
['encoder'
,'decoder'
,'none'
,'both'
] (default:'both'
)) – Whether to use batch norm in layersuse_layer_norm (
Literal
['encoder'
,'decoder'
,'none'
,'both'
] (default:'none'
)) – Whether to use layer norm in layerslinear_classifier – If
True
, uses a single linear layer for classification instead of a multi-layer perceptron.**vae_kwargs – Keyword args for
VAE
Attributes table#
Methods table#
|
Compute the loss. |
Attributes#
- SCANVAE.training: bool#