flashscenic: Accelerated GRN Analysis with RegDiffusion for Large-Scale Single-Cell Data

flashscenic delivers a 12- to 27-fold downstream speedup over pySCENIC for SCENIC GRN analysis while preserving strict numerical equivalence (Pearson r = 0.9996).

• Why it matters: Existing pySCENIC workflows are sequential and CPU-bound, limiting scalability and making repeated runs to capture network stochasticity computationally prohibitive.
• What they did: The framework reformulates module construction, cisTarget motif pruning, and AUCell scoring as batched GPU tensor operations, then benchmarks nine consensus aggregation strategies across multiple tissue cohorts.
• The result: In an acute myeloid leukemia (AML) atlas of 116,043 cells, uncorrected TF activity provides an intrinsic unsupervised batch-integration benefit over raw PCA, and adding it to a standard method matches or exceeds expression-based integration while recovering CEBPA and HOXA9.

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