AnnoAudit: a marker-based protocol for auditing single-cell atlas annotations reveals annotation-driven artifacts in a widely used traumatic brain injury resource
- 1Zhangjiagang First People's Hospital (Suchoow University Affiliated Zhangjiagang Hospital)
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]
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BibTeX
@misc{zhang2026annoaudit,
title = {{AnnoAudit: a marker-based protocol for auditing single-cell atlas annotations reveals annotation-driven artifacts in a widely used traumatic brain injury resource}},
author = {L. Zhang and H. Rao and M. Li and X. Qian and Y. Zhang and Q. Yan and R. Gao},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.03.749125},
url = {https://www.biorxiv.org/content/10.64898/2026.09.03.749125v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - AnnoAudit: a marker-based protocol for auditing single-cell atlas annotations reveals annotation-driven artifacts in a widely used traumatic brain injury resource
AU - L. Zhang
AU - H. Rao
AU - M. Li
AU - X. Qian
AU - Y. Zhang
AU - Q. Yan
AU - R. Gao
PY - 2026
DA - 2026/09/03
PB - bioRxiv
DO - 10.64898/2026.09.03.749125
UR - https://www.biorxiv.org/content/10.64898/2026.09.03.749125v1
AB - 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]
ER -