LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation

Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data. Understanding these networks is crucial for uncovering disease mechanisms and identifying therapeutic targets. In this work, we investigate the potential of large language models (LLMs) for GRN discovery, leveraging their learned biological knowledge alone or in combination with traditional statistical methods. We develop a task-based evaluation strategy to address the challenge of unavailable ground truth causal graphs. Specifically, we use the GRNs suggested by LLMs to guide causal synthetic data generation and compare the resulting data against the original dataset. Our statistical and biological assessments show that LLMs can support statistical modeling and data synthesis for biological research. Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technology that enables the collection of gene expression data from individual cells. This approach opens up new avenues for a wide range of scientific and clinical applications. One crucial application of scRNA-seq data is the reconstruction and analysis of gene regulatory networks (GRNs), which represent the interactions between genes. GRN analysis can deepen our understanding of disease mechanisms, identify key regulatory pathways, and provide a foundation for the development of interventional gene therapies and targeted drug discovery. Statistical causal discovery algorithms (Scheines et al., 1998; Zheng et al., 2018; Mercatelli et al., 2020; Brouillard et al., 2020; Lippe et al., 2021; Yu & Welch, 2022; Roohani et al., 2024) can reveal potential causal links between TFs and their target gene. However, they often lack robustness and are prone to detecting spurious correlations, especially in high-dimensional, noisy single-cell data. Furthermore, many of these approaches rely heavily on prior knowledge from curated databases (e.g., TRANSFAC (Wingender et al., 1996), RegNetwork (Liu et al., 2015), ENCODE (de Souza, 2012), BioGRID (de Souza, 2012), and AnimalTFDB (Hu et al., 2019)), which frequently lack essential contextual information such as specific cell types or conditions, leading to inaccuracies in the inferred regulatory relationships (Zinati et al., 2024). Most of the above methods involve the refinement of the statistically inferred causal graph by LLM.