Abstract
Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.
Publication types
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Research Support, N.I.H., Extramural
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Research Support, Non-U.S. Gov't
MeSH terms
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Algorithms
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Animals
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Brain Mapping
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Cluster Analysis
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Computational Biology / methods
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Computer Simulation
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Databases, Genetic*
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Deep Learning*
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Gene Expression Profiling*
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Inflammation
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Intestines / cytology
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Leukocytes, Mononuclear / cytology
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Mice
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Phenotype
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Principal Component Analysis
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RNA / analysis
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RNA / genetics*
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Reproducibility of Results
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Retina / metabolism
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Sequence Analysis, RNA
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Single-Cell Analysis*
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Software
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Transcriptome*