TFcomb identifies transcription factor combinations for cellular reprogramming based on single-cell multiomics data.

ABSTRACT: Reprogramming cell state transitions provides the potential for cell engineering and regenerative therapy. Finding the reprogramming transcription factors (TFs) and their combinations that can direct the desired state transition is crucial for the task. Computational methods have been developed to identify such reprogramming TFs. However, most of them can only generate a ranked list of individual TFs and ignore the identification of TF combinations. Even for individual reprogramming TF identification, current methods often fail to put the real effective reprogramming TFs at the top. To address these challenges, we developed TFcomb, a computational method that leverages single-cell multiomics data to identify reprogramming TFs and TF combinations. We modeled the task of finding reprogrammin

SUBMITTER: Li C 

PROVIDER: S-EPMC12129019 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature