LDVAE¶
-
class
scvi.models.
LDVAE
(n_input, n_batch=0, n_labels=0, n_hidden=128, n_latent=10, n_layers_encoder=1, dropout_rate=0.1, dispersion='gene', log_variational=True, reconstruction_loss='nb', use_batch_norm=True, bias=False, latent_distribution='normal')[source]¶ Bases:
scvi.models.vae.VAE
Linear-decoded Variational auto-encoder model.
Implementation of [Svensson20].
This model uses a linear decoder, directly mapping the latent representation to gene expression levels. It still uses a deep neural network to encode the latent representation.
Compared to standard VAE, this model is less powerful, but can be used to inspect which genes contribute to variation in the dataset. It may also be used for all scVI tasks, like differential expression, batch correction, imputation, etc. However, batch correction may be less powerful as it assumes a linear model.
- Parameters
n_hidden (
int
int
) – Number of nodes per hidden layer (for encoder)n_layers_encoder (
int
int
) – Number of hidden layers used for encoder NNsdropout_rate (
float
float
) – Dropout rate for neural networksOne 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
bool
) – Log(data+1) prior to encoding for numerical stability. Not normalization.reconstruction_loss (
str
str
) –One of
'nb'
- Negative binomial distribution'zinb'
- Zero-inflated negative binomial distribution
use_batch_norm (
bool
bool
) – Bool whether to use batch norm in decoderbias (
bool
bool
) – Bool whether to have bias term in linear decoder
Methods Summary
Extract per-gene weights (for each Z, shape is genes by dim(Z)) in the linear decoder.
Methods Documentation