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.
Show abstract
Single-cell RNA sequencing reveals cellular heterogeneity, but linking cellular states to individual-level phenotypes remains challenging. We present scVIP, a generative framework that integrates transcriptional profiles and phenotypic markers to learn personalized individual-level embeddings using generative models and cell-type-aware multi-instance learning. scVIP predicts developmental age, disease progression, and neuropathology, while harmonizing datasets with distinct phenotype definitions. The model highlights disease-relevant cell populations and transcriptional programs underlying neurodegeneration.
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