Table 1.

Example studies combining experimental and statistical techniques to isolate phenotypic variability from other sources of variation.

Reference	Major Question	Experimental platform	How did the experimental design isolate sources of variation?	How did the statistical methods isolate sources of variation?
Rinnot et al.PNAS2011[37]	How much cell-to-cell variability in protein levels is due to stochastic events?	Flow cytometry	A fluorescent 2-reporter system distinguishes global variability, which coordinately affects reporters, from stochastic variability, which independently affects reporters (data from [36]).	The residuals from plots of fluorescence mean vs. CV are utilized to distinguish effects on variability from effects on mean.
Levy et al.PloS Biology2012 [7]	Does variation in singlecell growth and gene expression correlate with survival of acute stress?	High content imaging (HCI)	An experimental design similar to that in Figure 2 quantifies effects on growth variability from instrument error, genotypic differences, and clonal heterogeneity.	GLM is used to estimate the relative effect on heat-shock survival from clonal heterogeneity in growth rates vs. genotypic differences.
Kim et al. Genome Biology2013 [50]	Can expression-level variability present in mouse embryonic stem (ES) cells be explained by a kinetic model for transcriptional bursting?	Single-cell RNA-seq	Correlations between expression-level variability (data from [51]) and histone modifications (data from [78]) suggest a biological basis for cell-to-cell variation in gene expression.	A Poisson-Beta distribution is used to model the kinetics of stochastic gene expression caused by transcriptional bursting. Single-cell RNA-seq data from mouse ES cells fits this model.