Work

AnnoAudit: a marker-based protocol for auditing single-cell atlas annotations, with a self-audit case

Year: 2026

Type: preprint

Abstract: Motivation Single-cell atlas annotations are routinely used as ground truth without validation. We present AnnoAudit, a marker-based protocol with four convergent checks and an ambient-aware discrimination step (D1) that audits labels before re-analysis. Results We applied AnnoAudit to labels generated by our own labelling pipeline for a public TBI single-cell dataset (CEREBRI, GSE269748). The audit exposed a permissive fallback rule that assigns a glutamatergic identity to any pan-neuronal-marker-positive cell lacking a subtype marker, manufacturing a 45,051-cell population that four independent reference-mapping models assign predominantly to choroid plexus (46.2%), astrocytes (22.4%) and microglia/macrophage (10.7%), with only 1.2% receiving any neuronal assignment (all inhibitory) and none assigned to an excitatory class. Marker- and depth-based controls show that the count matrices we analysed contain no neuronal-level expression of canonical neuronal genes (maximum Snap25 count 14 across 342,857 cells; Slc17a7 >= 5 in 5 cells), with transthyretin accounting for ~4% of all UMIs. The case illustrates the failure mode the protocol is designed to detect: permissive labelling rules can manufacture plausible-looking neuronal populations from data that lack neuronal-level signal. A simulation benchmark delimits the protocol’s ambient boundary, and a literature screen identified six published re-analyses of the same dataset, none of which validated the annotations they inherited. No claim is made in this version about the cell-type annotations of any other resource . Availability and implementation AnnoAudit is implemented in Python (scanpy); MIT-licensed source at github.com/ronnygao/AnnoAudit (doi:10.5281/zenodo.22274752). Contact [email protected]

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.64898/2026.09.03.749125