Manifold‑Informed Gene‑module Extraction for Disentangling Simultaneous Dynamics in scRNA‑Seq

Thomas M. Menino Convention & Exhibition Center 

We introduce MIRAGE, a statistical framework using cell-cell manifolds to test whether gene sets encode the same or distinct cellular dynamics. MIRAGE constructs manifolds for many gene sets, quantifies their geometric similarity, and uses hypothesis testing to merge only sets sharing common cell–cell geometry, identifying independent or co-occurring cellular dynamics. Applied to mouse pancreatic endocrinogenesis, MIRAGE separates developmental and cell-cycle programs, and in fibroblast reprogramming datasets, yields reproducible gene modules tracking shared intermediate states. In human Alzheimer's disease microglia, where heterogeneous stimuli produce overlapping homeostatic, inflammatory, and metabolic states, MIRAGE identifies one module capturing a within-donor continuum in both AD and non-AD brains, and a second branching module with one arm enriched for hypoxia and lysosomal stress genes, preferentially occupied by donors with elevated amyloid/tau burden and cognitive impairment. MIRAGE offers a principled approach for decomposing single-cell data into pathway-level manifolds, clarifying how co-occurring cellular programs contribute to disease-relevant heterogeneity.

Gene clustering

Geometric structure

Nearest neighbor graph

Multivariate hypothesis testing 

Section on Statistics in Genomics and Genetics