Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data
AffiliationUniv Arizona, Dept Epidemiol & Biostat
Univ Arizona, Dept Biosyst Engn
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CitationSun, X., Liu, Y., & An, L. (2020). Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data. Nature Communications, 11(1), 1-9.
Rights© The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License.
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AbstractSingle-cell RNA sequencing (scRNA-seq) technologies allow researchers to uncover the biological states of a single cell at high resolution. For computational efficiency and easy visualization, dimensionality reduction is necessary to capture gene expression patterns in low-dimensional space. Here we propose an ensemble method for simultaneous dimensionality reduction and feature gene extraction (EDGE) of scRNA-seq data. Different from existing dimensionality reduction techniques, the proposed method implements an ensemble learning scheme that utilizes massive weak learners for an accurate similarity search. Based on the similarity matrix constructed by those weak learners, the low-dimensional embedding of the data is estimated and optimized through spectral embedding and stochastic gradient descent. Comprehensive simulation and empirical studies show that EDGE is well suited for searching for meaningful organization of cells, detecting rare cell types, and identifying essential feature genes associated with certain cell types. Dimensionality reduction is used to make the analysis of single-cell RNA sequencing data more efficient. Here the authors propose a method, EDGE, which simultaneously carries out dimensionality reduction and feature gene extraction.
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Except where otherwise noted, this item's license is described as © The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License.
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