Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues.

Clicks: 333
ID: 56020
2018
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Ranked #41 of 139 articles by views in methods in molecular biology (clifton, nj)

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Abstract
Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised methods to deconvolve tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we develop a novel unsupervised deconvolution method, Convex Analysis of Mixtures (CAM), within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tissue samples. To facilitate the utility of this method, we implement an R-Java CAM package that provides comprehensive analytic functions and graphic user interface (GUI).
Reference Key
wang2018mathematicalmethods Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Niya;Chen, Lulu;Wang, Yue;
Journal methods in molecular biology (clifton, nj)
Year 2018
DOI
10.1007/978-1-4939-7710-9_16
URL
Keywords
blind source separation convex analysis of mixture data deconvolution marker genes tissue heterogeneity

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