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

BMC Bioinformatics

Justin Feigelman¹  Fabian J Theis¹  Carsten Marr² 

[1] Department of Mathematics, Technische Universität München, Boltzmannstrasse, 3, 85747 Garching, Germany

[2] Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany

关键词: qPCR analysis;    Subpopulation identification;    Correlation;    Multiresolution;   

Others  :  1087553
DOI  :  10.1186/1471-2105-15-240

 received in 2014-04-23, accepted in 2014-07-04,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Feigelman et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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