The article introduces PHLOWER, a novel computational method for inferring complex, multi-branching cell differentiation trajectories from single-cell multimodal data, such as RNA sequencing and ATAC sequencing. By leveraging the harmonic component of the Hodge decomposition on simplicial complexes, PHLOWER translates cell differentiation into flow embeddings that facilitate the reconstruction of intricate differentiation trees. The authors demonstrate PHLOWER's superior performance through extensive benchmarking against competing methods using both simulated and real single-cell RNA-sequencing data, particularly showing strength in recovering complex structures. Furthermore, the study applies PHLOWER to analyze kidney organoid differentiation, successfully identifying major cell lineages and predicting key transcription factors (TFs), whose targeted silencing via siRNA was validated to reduce undesired off-target cells and enhance desired kidney cell populations.

References:

• Cheng, Mingbo, et al. "PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories." Nature Methods (2025): 1-9.