期刊
BIOINFORMATICS
卷 36, 期 16, 页码 4415-4422出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btaa293
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类别
资金
- Lundbeck Foundation [R190 2014-3904]
- Novo Nordisk Foundation [NNF18CC0034900, NNF16OC0021496]
- Danish Ministry of Higher Education and Science
Motivation: Models for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual data-processing steps, each with their own set of hyperparameter choices. With deep generative models one can work directly with count data, make likelihood-based model comparison, learn a latent representation of the cells and capture more of the variability in different cell populations. Results: We propose a novel method based on variational auto-encoders (VAEs) for analysis of single-cell RNA sequencing (scRNA-seq) data. It avoids data preprocessing by using raw count data as input and can robustly estimate the expected gene expression levels and a latent representation for each cell. We tested several count likelihood functions and a variant of the VAE that has a priori clustering in the latent space. We show for several scRNA-seq datasets that our method outperforms recently proposed scRNA-seq methods in clustering cells and that the resulting clusters reflect cell types.
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