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

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...

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Published inCurrent 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
LanguageEnglish
Published Elsevier Ltd 01.02.2018
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Abstract 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 understanding of cellular states and progression, they also pose new challenges and require advanced mathematical and algorithmic tools to extract underlying biological signals. In this review, we cover one of the most promising avenues of research into unlocking the potential of scRNA-seq data: the field of manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to process the data, extract its dynamics, and infer patterns in it. In particular, we cover manifold learning-based methods for denoising the data, revealing gene interactions, extracting pseudotime progressions with model fitting, visualizing the cellular state space via dimensionality reduction, and clustering the data.
AbstractList 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 understanding of cellular states and progression, they also pose new challenges and require advanced mathematical and algorithmic tools to extract underlying biological signals. In this review, we cover one of the most promising avenues of research into unlocking the potential of scRNA-seq data: the field of manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to process the data, extract its dynamics, and infer patterns in it. In particular, we cover manifold learning-based methods for denoising the data, revealing gene interactions, extracting pseudotime progressions with model fitting, visualizing the cellular state space via dimensionality reduction, and clustering the data.
Author Moon, Kevin R.
Burkhardt, Daniel
Wolf, Guy
van Dijk, David
Krishnaswamy, Smita
Stanley, Jay S.
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Keywords Gene regulatory networks
Pseudotime trends
Imputation
Manifold learning
Data mining
Single cell RNA sequencing
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Snippet Recent advances in single-cell RNA sequencing technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by...
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SubjectTerms Data mining
Gene regulatory networks
Imputation
Manifold learning
Pseudotime trends
Single cell RNA sequencing
Title Manifold learning-based methods for analyzing single-cell RNA-sequencing data
URI https://dx.doi.org/10.1016/j.coisb.2017.12.008
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