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:
/tutorials/notebooks/linear_decoder
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.
- 1
Valentine Svensson, Adam Gayoso, Nir Yosef, Lior Pachter (2020), Interpretable factor models of single-cell RNA-seq via variational autoencoders, Bioinformatics.