Getting started
Deep exponential families for single-cell data. scDEF learns hierarchies of cell states and their gene signatures from scRNA-seq data. The method can be used for dimensionality reduction, visualization, gene signature identification, clustering at multiple levels of resolution, and batch integration. The informed version (iscDEF) can additionally take known gene lists to jointly assign cells to types and find clusters within each type.
Installation
scDEF is available through PyPI:
pip install scdef
Please be sure to install a version of JAX that is compatible with your GPU (if applicable). scDEF is much faster on the GPU than on the CPU.
Optional: using the scdef.benchmark module
The scdef.benchmark module includes wrapper functions to other methods. If you wish to use it, please install the extras:
pip install scdef[extras]
The scdef.benchmark also contains a wrapper function to scVI from scvi-tools, but scvi-tools is not included in the extras and it must be installed separately if you wish to use it. The same applies to scHPF.
Example notebooks
To get started with scDEF, please see the example notebooks:
Contributors
Pedro Falé Ferreira @pedrofale