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• W2967990090 abstract "Single cell RNA-seq makes possible the investigation of variability in gene expression among cells, and dependence of variation on cell type. Statistical inference methods for such analyses must be scalable, and ideally interpretable. We present an approach based on a modification of a recently published highly scalable variational autoencoder framework that provides interpretability without sacrificing much accuracy. We demonstrate that our approach enables identification of gene programs in massive datasets. Our strategy, namely the learning of factor models with the auto-encoding variational Bayes framework, is not domain specific and may be of interest for other applications." @default.
• W2967990090 created "2019-08-22" @default.
• W2967990090 creator A5020008219 @default.
• W2967990090 creator A5020743154 @default.
• W2967990090 date "2019-08-16" @default.
• W2967990090 modified "2023-10-16" @default.
• W2967990090 title "Interpretable factor models of single-cell RNA-seq via variational autoencoders" @default.
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• W2967990090 doi "https://doi.org/10.1101/737601" @default.
• W2967990090 hasPublicationYear "2019" @default.
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