GAGER: gene regulatory network assisted gene expression restoration

Gene regulatory networks are crucial for cellular function, and disruptions in transcription factor (TF) regulation often lead to diseases. However, identifying TFs to transition a source cell state to a desired target state remains challenging. We present a method to identify key TFs whose perturbation can restore gene expressions in a source state to target levels. Its effectiveness is demonstrated on datasets from yeast TF knockouts, cardiomyocytes from hypoplastic left heart syndrome patients, and mouse models of neurodegeneration. The method accurately identifies knocked-out TFs in the yeast dataset. In the cardiomyocyte dataset, it pinpoints TFs that, though not differentially expressed in many cases, exert significant regulatory influence on downstream differentially expressed genes. Finally, in the mouse model dataset, it identifies disease stage-specific TFs, improving similarity between healthy and diseased states at various time points. Unlike traditional approaches relying on differential expression analysis, our method uses network-based prioritization for more targeted and biologically relevant TF selection. These findings highlight its potential as a therapeutic tool for precise TF targeting to normalize gene expressions in diseased states.