A Quantitative Framework for Evaluating Single-Cell Data Structure Preservation by Dimensionality Reduction Techniques

A Quantitative Framework for Evaluating Single-Cell Data Structure Preservation by Dimensionality Reduction Techniques

Summary: High-dimensional data, such as those generated by single-cell RNA sequencing (scRNA-seq), present challenges in interpretation and visualization. Numerical and computational methods for dimensionality reduction allow for low-dimensional representation of genome-scale expression data for dow...

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Bibliographic Details
Main Authors: Cody N. Heiser, Ken S. Lau
Format: Article
Language:English
Published: Elsevier 2020-05-01
Series:Cell Reports
Subjects:
single-cell transcriptomics
dimensionality reduction
visualization
single-cell analysis
data analysis
unsupervised learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2211124720305258

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http://www.sciencedirect.com/science/article/pii/S2211124720305258

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