Table 1.
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. |