scvi.module.PEAKVAE#
- class scvi.module.PEAKVAE(n_input_regions, n_batch=0, n_hidden=None, n_latent=None, n_layers_encoder=2, n_layers_decoder=2, n_continuous_cov=0, n_cats_per_cov=None, dropout_rate=0.1, model_depth=True, region_factors=True, use_batch_norm='none', use_layer_norm='both', latent_distribution='normal', deeply_inject_covariates=False, encode_covariates=False)[source]#
Bases:
BaseModuleClass
Variational auto-encoder model for ATAC-seq data.
This is an implementation of the peakVI model descibed in.
- Parameters:
n_input_regions (int) – Number of input regions.
n_batch (int) – Number of batches, if 0, no batch correction is performed.
n_hidden (Tunable_[int]) – Number of nodes per hidden layer. If
None
, defaults to square root of number of regions.n_latent (Tunable_[int]) – Dimensionality of the latent space. If
None
, defaults to square root ofn_hidden
.n_layers_encoder (Tunable_[int]) – Number of hidden layers used for encoder NN.
n_layers_decoder (Tunable_[int]) – Number of hidden layers used for decoder NN.
dropout_rate (Tunable_[float]) – Dropout rate for neural networks
model_depth (bool) – Model library size factors or not.
region_factors (bool) – Include region-specific factors in the model
use_batch_norm (Tunable_[Literal['encoder', 'decoder', 'none', 'both']]) –
One of the following
'encoder'
- use batch normalization in the encoder only'decoder'
- use batch normalization in the decoder only'none'
- do not use batch normalization (default)'both'
- use batch normalization in both the encoder and decoder
use_layer_norm (Tunable_[Literal['encoder', 'decoder', 'none', 'both']]) –
One of the following
'encoder'
- use layer normalization in the encoder only'decoder'
- use layer normalization in the decoder only'none'
- do not use layer normalization'both'
- use layer normalization in both the encoder and decoder (default)
latent_distribution (Tunable_[Literal['normal', 'ln']]) –
which latent distribution to use, options are
'normal'
- Normal distribution (default)'ln'
- Logistic normal distribution (Normal(0, I) transformed by softmax)
deeply_inject_covariates (Tunable_[bool]) – Whether to deeply inject covariates into all layers of the decoder. If False (default), covairates will only be included in the input layer.
n_continuous_cov (int) –
encode_covariates (bool) –
Attributes table#
Methods table#
|
Runs the generative model. |
|
Compute the reconstruction loss. |
|
Helper function used in forward pass. |
|
Compute the loss. |
Attributes#
training
Methods#
generative
- PEAKVAE.generative(z, qz_m, batch_index, cont_covs=None, cat_covs=None, use_z_mean=False)[source]#
Runs the generative model.
get_reconstruction_loss
inference
- PEAKVAE.inference(x, batch_index, cont_covs, cat_covs, n_samples=1)[source]#
Helper function used in forward pass.
loss