title: Estimating intrinsic and extrinsic noise from single-cell gene expression measurements.
abstract: Gene expression is stochastic and displays variation ("noise") both within and between cells. Intracellular (intrinsic) variance can be distinguished from extracellular (extrinsic) variance by applying the law of total variance to data from two-reporter assays that probe expression of identically regulated gene pairs in single cells. We examine established formulas [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): "Stochastic gene expression in a single cell," Science, 297, 1183-1186.] for the estimation of intrinsic and extrinsic noise and provide interpretations of them in terms of a hierarchical model. This allows us to derive alternative estimators that minimize bias or mean squared error. We provide a geometric interpretation of these results that clarifies the interpretation in [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): "Stochastic gene expression in a single cell," Science, 297, 1183-1186.]. We also demonstrate through simulation and re-analysis of published data that the distribution assumptions underlying the hierarchical model have to be satisfied for the estimators to produce sensible results, which highlights the importance of normalization.
authors.forename: Audrey Qiuyan
authors.initials: AQ
authors.lastname: Fu
authors.forename: Lior
authors.initials: L
authors.lastname: Pachter
mesh_terms: D003198:Computer Simulation; D020869:Gene Expression Profiling; D059010:Single-Cell Analysis; D013269:Stochastic Processes