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- W2901677030 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. scVI is a ready-to-use generative deep learning tool for large-scale single-cell RNA-seq data that enables raw data processing and a wide range of rapid and accurate downstream analyses." @default.
- W2901677030 created "2018-11-29" @default.
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- W2901677030 date "2018-11-30" @default.
- W2901677030 modified "2023-10-18" @default.
- W2901677030 title "Deep generative modeling for single-cell transcriptomics" @default.
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- W2901677030 doi "https://doi.org/10.1038/s41592-018-0229-2" @default.
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