TransGRN: a transfer learning-based framework for inferring gene regulatory networks across cell lines.

Abstract

Inferring gene regulatory networks (GRNs) is critical for understanding the mechanisms that govern cellular behavior. Advances in single-cell RNA sequencing (scRNA-seq) have enabled GRN analysis at single-cell resolution and stimulated the development of many computational methods. However, most existing approaches depend heavily on extensive prior regulatory information, which limits their effectiveness in few-shot settings where such data for the target cell type are scarce or unavailable. To address this challenge, we propose TransGRN, a transfer learning-based method for inferring gene regulatory networks (GRNs) across cell lines. TransGRN adopts a cross-cell-line pre-training strategy that combines scRNA-seq data from multiple source cell lines with biological knowledge obtained from large language models. In addition, it includes a regulatory interaction extraction module that integrates gene expression profiles with semantic information. By transferring generalizable gene-gene regulatory patterns from source to target cell lines, TransGRN achieves state-of-the-art performance in both benchmark tests and few-shot GRN inference tasks.