  Generative modeling of single-cell RNA-seq data has shown invaluable
potential in community-driven tasks such as trajectory inference, batch effect
removal and gene expression generation. However, most recent deep models
generating synthetic single cells from noise operate on pre-processed
continuous gene expression approximations, ignoring the inherently discrete and
over-dispersed nature of single-cell data, which limits downstream applications
and hinders the incorporation of robust noise models. Moreover, crucial aspects
of deep-learning-based synthetic single-cell generation remain underexplored,
such as controllable multi-modal and multi-label generation and its role in the
performance enhancement of downstream tasks. This work presents Cell Flow for
Generation (CFGen), a flow-based conditional generative model for multi-modal
single-cell counts, which explicitly accounts for the discrete nature of the
data. Our results suggest improved recovery of crucial biological data
characteristics while accounting for novel generative tasks such as
conditioning on multiple attributes and boosting rare cell type classification
via data augmentation. By showcasing CFGen on a diverse set of biological
datasets and settings, we provide evidence of its value to the fields of
computational biology and deep generative models.
