Deep generative modeling for single-cell transcriptomics. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. Nature Methods, 15(12):1053–1058, December, 2018. Publisher: Nature Publishing Group![Deep generative modeling for single-cell transcriptomics [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells (https://github.com/YosefLab/scVI). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task.
@article{lopez_deep_2018,
title = {Deep generative modeling for single-cell transcriptomics},
volume = {15},
copyright = {2018 The Author(s), under exclusive licence to Springer Nature America, Inc.},
issn = {1548-7105},
url = {https://www.nature.com/articles/s41592-018-0229-2},
doi = {10.1038/s41592-018-0229-2},
abstract = {Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells (https://github.com/YosefLab/scVI). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task.},
language = {en},
number = {12},
urldate = {2024-10-22},
journal = {Nature Methods},
author = {Lopez, Romain and Regier, Jeffrey and Cole, Michael B. and Jordan, Michael I. and Yosef, Nir},
month = dec,
year = {2018},
note = {Publisher: Nature Publishing Group},
keywords = {Computational biology and bioinformatics, Computational models, Unread},
pages = {1053--1058},
}
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