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Generative modeling of single-cell gene expression for dose-dependent chemical perturbations.


ABSTRACT: Single-cell sequencing reveals the heterogeneity of cellular response to chemical perturbations. However, testing all relevant combinations of cell types, chemicals, and doses is a daunting task. A deep generative learning formalism called variational autoencoders (VAEs) has been effective in predicting single-cell gene expression perturbations for single doses. Here, we introduce single-cell variational inference of dose-response (scVIDR), a VAE-based model that predicts both single-dose and multiple-dose cellular responses better than existing models. We show that scVIDR can predict dose-dependent gene expression across mouse hepatocytes, human blood cells, and cancer cell lines. We biologically interpret the latent space of scVIDR using a regression model and use scVIDR to order individual cells based on their sensitivity to chemical perturbation by assigning each cell a "pseudo-dose" value. We envision that scVIDR can help reduce the need for repeated animal testing across tissues, chemicals, and doses.

SUBMITTER: Kana O 

PROVIDER: S-EPMC10436058 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Generative modeling of single-cell gene expression for dose-dependent chemical perturbations.

Kana Omar O   Nault Rance R   Filipovic David D   Marri Daniel D   Zacharewski Tim T   Bhattacharya Sudin S  

Patterns (New York, N.Y.) 20230811 8


Single-cell sequencing reveals the heterogeneity of cellular response to chemical perturbations. However, testing all relevant combinations of cell types, chemicals, and doses is a daunting task. A deep generative learning formalism called variational autoencoders (VAEs) has been effective in predicting single-cell gene expression perturbations for single doses. Here, we introduce single-cell variational inference of dose-response (scVIDR), a VAE-based model that predicts both single-dose and mu  ...[more]

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