SCANVI

class scvi.models.SCANVI(n_input, n_batch=0, n_labels=0, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', log_variational=True, reconstruction_loss='zinb', y_prior=None, labels_groups=None, use_labels_groups=False, classifier_parameters={})

Bases: scvi.models.vae.VAE

Single-cell annotation using variational inference.

This is an implementation of the scANVI model descibed in [Xu19], inspired from M1 + M2 model, as described in (https://arxiv.org/pdf/1406.5298.pdf).

Parameters

• n_input (intint) – Number of input genes

• n_batch (intint) – Number of batches

• n_labels (intint) – Number of labels

• n_hidden (intint) – Number of nodes per hidden layer

• n_latent (intint) – Dimensionality of the latent space

• n_layers (intint) – Number of hidden layers used for encoder and decoder NNs

• dropout_rate (floatfloat) – Dropout rate for neural networks

• dispersion (strstr) –

  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 (boolbool) – Log(data+1) prior to encoding for numerical stability. Not normalization.

• reconstruction_loss (strstr) –

  One of

  • 'nb' - Negative binomial distribution

  • 'zinb' - Zero-inflated negative binomial distribution

• y_prior – If None, initialized to uniform probability over cell types

• labels_groups (Sequence[int], NoneOptional[Sequence[int]]) – Label group designations

• use_labels_groups (boolbool) – Whether to use the label groups

Returns

Examples

>>> gene_dataset = CortexDataset()
>>> scanvi = SCANVI(gene_dataset.nb_genes, n_batch=gene_dataset.n_batches * False,
... n_labels=gene_dataset.n_labels)

>>> gene_dataset = SyntheticDataset(n_labels=3)
>>> scanvi = SCANVI(gene_dataset.nb_genes, n_batch=gene_dataset.n_batches * False,
... n_labels=3, y_prior=torch.tensor([[0.1,0.5,0.4]]), labels_groups=[0,0,1])

Methods Summary

classify(x)
forward(x, local_l_mean, local_l_var[, …])	Returns the reconstruction loss and the KL divergences
get_latents(x[, y])	Returns the result of sample_from_posterior_z inside a list

Methods Documentation

classify(x)

forward(x, local_l_mean, local_l_var, batch_index=None, y=None)

Returns the reconstruction loss and the KL divergences

Parameters

• x – tensor of values with shape (batch_size, n_input)

• local_l_mean – tensor of means of the prior distribution of latent variable l with shape (batch_size, 1)

• local_l_var – tensor of variancess of the prior distribution of latent variable l with shape (batch_size, 1)

• batch_index – array that indicates which batch the cells belong to with shape batch_size (Default value = None)

• y – tensor of cell-types labels with shape (batch_size, n_labels) (Default value = None)

Returns

type the reconstruction loss and the Kullback divergences

get_latents(x, y=None)

Returns the result of sample_from_posterior_z inside a list

Parameters

• x – tensor of values with shape (batch_size, n_input)

• y – tensor of cell-types labels with shape (batch_size, n_labels) (Default value = None)

Returns

type one element list of tensor