GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks.

ABSTRACT: We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new condi

SUBMITTER: Zinati Y 

PROVIDER: S-EPMC11525796 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature