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Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.

Proc. Natl. Acad. Sci. U.S.A. 111, E626-E635 (2014)
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Cell-to-cell variations in gene regulation occur in a number of biological contexts, such as development and cancer. Discovering regulatory heterogeneities in an unbiased manner is difficult owing to the population averaging that is required for most global molecular methods. Here, we show that we can infer single-cell regulatory states by mathematically deconvolving global measurements taken as averages from small groups of cells. This averaging-and-deconvolution approach allows us to quantify single-cell regulatory heterogeneities while avoiding the measurement noise of global single-cell techniques. Our method is particularly relevant to solid tissues, where single-cell dissociation and molecular profiling is especially problematic.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Breast Cancer ; Morphogenesis ; Noise ; Systems Biology; Human Breast-cancer; Messenger-rna-seq; Single-cell; Gene-expression; Saccharomyces-cerevisiae; Noise; Reveals; Tissues; Morphogenesis; Proliferation
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