Chapter title |
Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues
|
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Chapter number | 16 |
Book title |
Transcriptome Data Analysis
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7710-9_16 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7709-3, 978-1-4939-7710-9
|
Authors |
Niya Wang, Lulu Chen, Yue Wang |
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). |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 2 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Unspecified | 1 | 50% |
Student > Bachelor | 1 | 50% |
Readers by discipline | Count | As % |
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Unspecified | 1 | 50% |
Agricultural and Biological Sciences | 1 | 50% |