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 reprogramming TFs and their combinations as an inverse problem, and used Tikhonov regularization to guarantee the generalization ability of solutions. For the coefficient matrix of the model, we designed a graph attention network to augment gene regulatory networks built with single-cell RNA-seq and ATAC-seq data. Benchmarking experiments on data of human embryonic stem cells demonstrate superior performance of TFcomb against existing methods for identifying individual TFs. We curate data sets of multiple cell reprogramming cases and demonstrate that TFcomb can efficiently identify reprogramming TF combinations from vast potential combinations. We apply TFcomb on a data set of mouse hair follicle development and find key TFs in cell differentiation. All experiments show that TFcomb is powerful in identifying reprogramming TFs and TF combinations from single-cell data sets to empower future cell engineering.