Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.

ABSTRACT: The development of single-cell RNA sequencing (scRNA-seq) technology provides valuable data resources for inferring gene regulatory networks (GRNs), enabling deeper insights into cellular mechanisms and diseases. While many methods exist for inferring GRNs from static scRNA-seq data, current approaches face challenges in accurately handling time-series scRNA-seq data due to high noise levels and data sparsity. The temporal dimension introduces additional complexity by requiring models to capture dynamic changes, increasing sensitivity to noise, and exacerbating data sparsity across time points. In this study, we introduce GRANGER, an unsupervised deep learning-based method that integrates multiple advanced techniques, including a recurrent variational autoencoder, GRANGER causality, sparsi

SUBMITTER: Chen L 

PROVIDER: S-EPMC11891664 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature