# LDVAE¶

**LDVAE** 1 (Linearly-decoded Variational Auto-encoder, also called Linear scVI; Python class `LinearSCVI`

)
is a flavor of scVI with a linear decoder.

The advantages of LDVAE are:

Can be used to interpret latent dimensions with factor loading matrix.

Scalable to very large datasets (>1 million cells).

The limitations of LDVAE include:

Less capacity than scVI, which uses a neural network decoder.

Less capable of integrating data with complex batch effects.

Tutorials:

## Contrasting with scVI¶

Here we discuss the differences between LDVAE and scVI.

In LDVAE, \(f_w(z_n, s_n)\) is a linear function, and thus can be represented by a matrix \(W\) of dimensions \(G\) (genes) by \((d + k)\) (latent space dim plus covariate categories).

This matrix \(W\) can be accessed using

`get_loadings()`

LDVAE does not offer transfer learning capabilities currently.

References:

- 1
Valentine Svensson, Adam Gayoso, Nir Yosef, Lior Pachter (2020),

*Interpretable factor models of single-cell RNA-seq via variational autoencoders*, Bioinformatics.