Unveiling Gene Regulatory Network Dynamics using Fuzzy Clustering

Abstract Partitioning cells into robust, reproducible clusters is a core step across single-cell-resolution analyses; current state-of-the-art approaches struggle with capturing and summarising dynamics on continuous expression patterns. We present Flufftail ( F uzzy L ogic U nifying F ramework reveals T ranscriptional A rchitectures summarised via I ntegrated L earning), an R framework and interactive Shiny app that consolidates clustering uncertainty by aggregating iterative stochastic partitions, resulting from a constant input. Flufftail computes per-cell membership probabilities, element-centric consistency scores, consensus matrices, and collapsed hard/crisp cell-assignments; we also leverage fuzzy assignments to prioritise genes that might act as regulatory hubs, subsequently using these as anchor points to infer gene regulatory network dynamics across transitions. We showcase the approach on single-nuclei and spatial transcriptomic case studies, illustrating how fuzzy clustering highlights transitional cell populations, proposing an ordered, state-dependent rewiring of regulatory interactions directly linked to the observed phenotype.