MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data
This method addresses the challenge of subpopulation identification in low-dimensional gene expression data for researchers in computational biology, though it appears incremental as it builds on existing correlation-based approaches.
The authors tackled the problem of identifying subpopulations in biological data with overlapping distributions by developing Multiresolution Correlation Analysis (MCA), a graphical tool that visually identifies subpopulations based on local pairwise correlations without predefined scales, and demonstrated its effectiveness on simulated and published single-cell qPCR data by recovering known subpopulations and revealing new insights.
Background: Biological data often originate from samples containing mixtures of subpopulations, corresponding e.g. to distinct cellular phenotypes. However, identification of distinct subpopulations may be difficult if biological measurements yield distributions that are not easily separable. Results: We present Multiresolution Correlation Analysis (MCA), a method for visually identifying subpopulations based on the local pairwise correlation between covariates, without needing to define an a priori interaction scale. We demonstrate that MCA facilitates the identification of differentially regulated subpopulations in simulated data from a small gene regulatory network, followed by application to previously published single-cell qPCR data from mouse embryonic stem cells. We show that MCA recovers previously identified subpopulations, provides additional insight into the underlying correlation structure, reveals potentially spurious compartmentalizations, and provides insight into novel subpopulations. Conclusions: MCA is a useful method for the identification of subpopulations in low-dimensional expression data, as emerging from qPCR or FACS measurements. With MCA it is possible to investigate the robustness of covariate correlations with respect subpopulations, graphically identify outliers, and identify factors contributing to differential regulation between pairs of covariates. MCA thus provides a framework for investigation of expression correlations for genes of interests and biological hypothesis generation.