# LDVAE¶

LDVAE 1 (Linearly-decoded Variational Auto-encoder, also called Linear scVI; Python class LinearSCVI) is a flavor of scVI with a linear decoder.

• 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.