AptamerBench: Auditing Generalization Across Patients and Cross-Modal Correspondence in Paired Single-Cell Profiling

Accurate prediction in unseen patients does not by itself establish subtype discrimination within patients or the value of pairing measurements from the same cell. We introduce AptamerBench, a benchmark for cell typing from single-cell aptamer-binding (APT) profiles, with 361,792 paired cells across 40 participants, 293 aptamer features, and an operational RNA-derived hierarchy of five lineages and 27 subtypes. AptamerBench combines patient-disjoint evaluation, subject-balanced scoring, and controls retrained on shuffled APT profiles. Standard APT-only models achieve low subtype performance in new patients. With true-lineage decoding applied to both conditions, an MLP trained on real APT exceeds its matched control retrained on profiles shuffled within each patient and lineage by 0.160 in subject-balanced Macro-F1 (95% CI [0.132, 0.172]). This contrast remains positive under joint disease/acquisition-group holdout. High conditional scores can nevertheless coexist with poor discrimination within patients. Under the original patient-disjoint folds, real APT, shuffled APT, and context from other cells of the same patient each achieve approximately 0.87 subject-balanced Macro-F1 when distinguishing Memory CD8T-1 from NK cells, while the context model has zero minority-class recall in every patient. An RNA+APT gain over the RNA reference is not reproduced under group holdout, and the tested controls do not resolve an additional classification benefit from exact same-cell pairing. Label-independent retrieval nevertheless detects same-cell RNA–APT correspondence in held-out patients. An exploratory multicenter RNA/ADT analysis reproduces the same qualitative separation. AptamerBench provides reusable tests that distinguish generalization to new patients, conditional subtype association, within-patient discrimination, modality benefit, pairing-specific predictive value, and detectable cross-modal correspondence.