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      Diffusion maps for high-dimensional single-cell analysis of differentiation data.

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          Abstract

          Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.

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          Author and article information

          Journal
          Bioinformatics
          Bioinformatics (Oxford, England)
          1367-4811
          1367-4803
          Sep 15 2015
          : 31
          : 18
          Affiliations
          [1 ] Institute of Computational Biology, Helmholtz Zentrum München 85764 Neuherberg, Germany and Department of Mathematics, Technische Universität München 85748 Garching, Germany Institute of Computational Biology, Helmholtz Zentrum München 85764 Neuherberg, Germany and Department of Mathematics, Technische Universität München 85748 Garching, Germany.
          [2 ] Institute of Computational Biology, Helmholtz Zentrum München 85764 Neuherberg, Germany and Department of Mathematics, Technische Universität München 85748 Garching, Germany.
          Article
          btv325
          10.1093/bioinformatics/btv325
          26002886
          ec15f2ac-cd35-4eea-918c-6a9de6686271
          © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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