DELPHAI predicts heterogeneous perturbation responses with learned single-cell fitness
Year: 2026
Type: preprint
Abstract: Current perturbation response modelling in single-cell transcriptomics assumes conserved cell mass and loses gene expression information to latent-space decoding. We propose DELPHAI, training a fitness network and an optimal transport network jointly without biological priors, and during inference applying a fitness-gated transport with a direct gene-space retrieval. Demonstrated across two benchmark frameworks, DELPHAI ranks first in predicting differentially expressed genes, while revealing which cell lineages a perturbation depletes.
Authors: Xian Zhang, Hui Wu, Haikun Liu
Institution: German Cancer Research Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7167499121