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Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data

Justin Feigelman
Fabian J. Theis
Carsten Marr

Multiresolution Correlation Analysis (MCA) is a method for visually identifying subpopulations based on the local pairwise correlation between covariates, without needing to define an a priori interaction scale. MCA facilitates the identification of differentially regulated subpopulations in simulated data from a small gene regulatory network. MCA recovers previously identified subpopulations, provides additional insight into the underlying correlation structure, reveals potentially spurious compartmentalizations, and provides insight into novel subpopulations. 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.

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Institution: Helmholtz Zentrum Munchen; Department of Mathematics, Technical University of Munich