Ranking reprogramming factors for cell differentiation

A comparison of nine computational methods for identification of reprogramming factors for cell differentiation. Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.

Automated summary

This study compares nine computational methods for identifying reprogramming factors for cell differentiation. The researchers found that methods using chromatin accessibility performed the best, with an average identification rate of 50-60% of reprogramming factors in the top ten candidates. Among the chromatin accessibility methods, DeepAccess and diffTF showed the highest correlation with the ranked significance of transcription factor candidates. AME and diffTF were identified as optimal methods for transcription factor recovery, allowing for systematic prioritization of candidates in the design of new reprogramming protocols.