A Julia package for fitting VAEs to single-cell data using count distributions. Based on the Python implementation in the
The scVI model was first proposed in Lopez R, Regier J, Cole MB et al. Deep generative modeling for single-cell transcriptomics. Nat Methods 15, 1053-1058 (2018).
More on the much more extensive Python package ecosystem
scvi-tools can be found on the website and in the corresponding paper Gayoso A, Lopez R, Xing G. et al. A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol 40, 163–166 (2022).
This is the documentation for the Julia version implementing basic functionality, including the following (non-exhausive list):
- standard and linearly decoded VAE models
- support for negative binomial generative distribution with and without zero-inflation
- different ways of specifying the dispersion parameter
- store data in a (very basic) Julia version of the Python
- several built-in datasets
- training routines supporting a wide range of customisable hyperparameters
The package can be downloaded from the Github repo and added with the Julia package manager via
julia> ] pkg > add "https://github.com/maren-ha/scVI.jl"
or alternatively by
julia> using Pkg; Pkg.add(url="https://github.com/maren-ha/scVI.jl")