scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes

Lai, H.-Y.; Yoo, Y.; Tjaernberg, A.; Travaglini, K. J.; Agrawal, A.; Kana, O.; van Velthoven, C.; Carroll, J. B.; Qiao, Q.; Mukherjee, S.; Fardo, D. W.; Lein, E.; Gabitto, M. I.

doi:10.64898/2026.04.20.717759

bioRxiv · 10.64898/2026.04.20.717759

scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes

Lai, H.-Y.·Yoo, Y.·Tjaernberg, A.·Travaglini, K. J.·Agrawal, A.·Kana, O.·van Velthoven, C.·Carroll, J. B.·Qiao, Q.·Mukherjee, S.·Fardo, D. W.·Lein, E.·Gabitto, M. I.

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Abstract

Single-cell transcriptomics resolves cellular heterogeneity within individuals, but connecting molecular states to individual-level phenotypes requires frameworks that explicitly bridge these scales. We present scVIP, a generative model that links gene expression, cell-type composition, and phenotypic measurements within a single probabilistic model, which enables accurate phenotype prediction and interpretable trajectory inference. A cell-type-aware multi-instance learning architecture learns donor embeddings that capture progression while localizing phenotype-associated signals to specific cell populations. Applied across four settings, scVIP accurately predicts cortical developmental age (Pearson r = 0.95), characterizes Huntingtons disease progression (concordance correlation coefficient = 0.90), integrates two Alzheimers disease cohorts recovering disease-relevant microglial and astrocytic programs, and distinguishes healthy from ACPA-positive individuals and non-progressors from early RA individuals, identifying inflammatory T cell programs associated with disease. scVIP enables principled analysis of how cellular states collectively shape organism-level phenotypes across development and disease.

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Lai, H.-Y., Yoo, Y., Tjaernberg, A., Travaglini, K. J., Agrawal, A., Kana, O., van Velthoven, C., Carroll, J. B., Qiao, Q., Mukherjee, S., Fardo, D. W., Lein, E., Gabitto, M. I.. 2026-04-22. scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes. https://doi.org/10.64898/2026.04.20.717759

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