Abstract
Oscillatory gene expression is fundamental to development, but technologies for monitoring expression oscillations are limited. We have developed a statistical approach called Oscope to identify and characterize the transcriptional dynamics of oscillating genes in single-cell RNA-seq data from an unsynchronized cell population. Applying Oscope to a number of data sets, we demonstrated its utility and also identified a potential artifact in the Fluidigm C1 platform.
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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Analysis of Variance
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Data Interpretation, Statistical*
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Embryonic Stem Cells / physiology
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Gene Expression Profiling / methods
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Gene Expression Profiling / statistics & numerical data
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Humans
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Models, Genetic*
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Real-Time Polymerase Chain Reaction / methods
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Sequence Analysis, RNA / methods*
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Sequence Analysis, RNA / statistics & numerical data
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Single-Cell Analysis / methods*
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Single-Cell Analysis / statistics & numerical data
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Software