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. Author manuscript; available in PMC: 2020 Jan 28.
Published in final edited form as: Analyst. 2019 Jan 28;144(3):794–807. doi: 10.1039/c8an01574k

Figure 1. Limitations of bulk cell analysis vs benefits of single cell analysis.

Figure 1.

(A) Population averages can mask cell heterogeneity. The mean measurement (indicated by dashed lines) of a population may not capture (i) the shaded tail of the distribution, (ii) a subpopulation or (iii) majority of the cells in case of bimodal behavior. (iv) Univariate analysis of a single measurement from individual cells may not be able to distinguish correlated (left) or anticorrelated (right) expression of cells (f1 and f2 indicate single cell measurements). (B) Interpreting functional significance from heterogeneity. (i) Individual cells can be represented as points in a feature space (ii) Cells can be partitioned into subpopulations (eg: S1 and S2) in regions of the feature space (iii) The presence of significant differences between subpopulations or ensemble averages can be tested. One can assess how informative is an entire decomposition of heterogeneity (middle and right). Reproduced from Altschuler and Wu1 with permission from Elsevier, Copyright 2010.