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

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          Abstract

          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.

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          Most cited references 30

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          Nature, nurture, or chance: stochastic gene expression and its consequences.

          Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
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            Principal component analysis

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              Nanog safeguards pluripotency and mediates germline development.

              Nanog is a divergent homeodomain protein found in mammalian pluripotent cells and developing germ cells. Deletion of Nanog causes early embryonic lethality, whereas constitutive expression enables autonomous self-renewal of embryonic stem cells. Nanog is accordingly considered a core element of the pluripotent transcriptional network. However, here we report that Nanog fluctuates in mouse embryonic stem cells. Transient downregulation of Nanog appears to predispose cells towards differentiation but does not mark commitment. By genetic deletion we show that, although they are prone to differentiate, embryonic stem cells can self-renew indefinitely in the permanent absence of Nanog. Expanded Nanog null cells colonize embryonic germ layers and exhibit multilineage differentiation both in fetal and adult chimaeras. Although they are also recruited to the germ line, primordial germ cells lacking Nanog fail to mature on reaching the genital ridge. This defect is rescued by repair of the mutant allele. Thus Nanog is dispensible for expression of somatic pluripotency but is specifically required for formation of germ cells. Nanog therefore acts primarily in construction of inner cell mass and germ cell states rather than in the housekeeping machinery of pluripotency. We surmise that Nanog stabilizes embryonic stem cells in culture by resisting or reversing alternative gene expression states.
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                Author and article information

                Affiliations
                [1 ]Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany
                [2 ]Department of Mathematics, Technische Universität München, Boltzmannstrasse, 3, 85747 Garching, Germany
                Contributors
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2014
                11 July 2014
                : 15
                : 240
                25015590 4227291 1471-2105-15-240 10.1186/1471-2105-15-240
                Copyright © 2014 Feigelman et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                Research Article

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