Journal
NATURE MACHINE INTELLIGENCE
Volume 2, Issue 11, Pages 693-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s42256-020-00244-4
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Funding
- National Key RAMP
- D Program of China [2018YFC0910402, 2018YFC1003102, 2017YFC0908402]
- Strategic Priority Research Program of the Chinese Academy of Sciences [E0XD842201]
- National Natural Science Foundation of China [32070795, 61673070]
- Open Project of Key Laboratory of Genomic and Precision Medicine, Chinese Academy of Sciences
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Single-cell RNA sequencing (scRNA-seq) technologies are used to characterize the heterogeneity of cells in cell types, developmental stages and spatial positions. The rapid accumulation of scRNA-seq data has enabled single-cell-type labelling to transform single-cell transcriptome analysis. Here we propose an interpretable deep-learning architecture using capsule networks (called scCapsNet). A capsule structure (a neuron vector representing a set of properties of a specific object) captures hierarchical relations. By utilizing competitive single-cell-type recognition, the scCapsNet model is able to perform feature selection to identify groups of genes encoding different subcellular types. The RNA expression signatures, which enable subcellular-type recognition, are effectively integrated into the parameter matrices of scCapsNet. This characteristic enables the discovery of gene regulatory modules in which genes interact with each other and are closely related in function, but present distinct expression patterns.
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