Manifold learning-based methods for analyzing single-cell RNA-sequencing data

• Published in: Current opinion in systems biology Vol. 7; pp. 36 - 46
• Main Authors: Moon, Kevin R., Stanley, Jay S., Burkhardt, Daniel, van Dijk, David, Wolf, Guy, Krishnaswamy, Smita
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2018
• Subjects: Data mining; Gene regulatory networks; Imputation; Manifold learning; Pseudotime trends; Single cell RNA sequencing; Gene regulatory networks; Pseudotime trends; Imputation; Manifold learning; Data mining; Single cell RNA sequencing

Recent advances in single-cell RNA sequencing technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression for thousands of cells in a single experiment. While these technologies hold great potential for improving our understandi...