PRISM: probabilistic cell-type resolution with integrated spatial modelling for simultaneous ambient RNA decontamination and doublet correction in spatial transcriptomics

Authors

Cupaioli, F., Merelli, I.

Abstract

Spatial transcriptomics count matrices are confounded by ambient RNA contamination and cell-segmentation doublets, two artifacts that co-occur but are rarely modelled jointly. We present PRISM, an Expectation-Maximization framework that simultaneously (i) assigns each cell a probabilistic cell-type identity, (ii) detects doublets via log-likelihood ratio testing of two-component mixtures, and (iii) estimates a per-cell contamination fraction anchored to a spatially resolved ambient profile. In an in silico benchmark of 40,800 simulated cells, PRISM recovers with Pearson r=1.000 across all contamination levels (0.05 to 0.50) and UMI depths (500 to 5,000), with zero doublet false positives and 100% singlet accuracy. Applied to a Visium HD mouse cerebellum dataset (four samples, 125,289 cells, 17 reference types) from a metachromatic leukodystrophy (MLD) model, PRISM identifies a median =0.60, flags 67.4% doublets (driven by the densely packed cerebellar granule layer), eliminates spurious myelin signal from microglia (0.82% to 0.00%), and doubles oligodendrocyte myelin purity (2.8% to 5.6%). PRISM runs on commodity CPUs in under 15 minutes and returns decontaminated integer count matrices for every measured gene.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 10 Nov 2026.

Stats

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.