A robust nonlinear low-dimensional manifold for single cell RNA-seq data

A robust nonlinear low-dimensional manifold for single cell RNA-seq data

Abstract Background Modern developments in single-cell sequencing technologies enable broad insights into cellular state. Single-cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, and developmental trajectories to broaden our understanding of cellular heterogeneity in tissues...

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Bibliographic Details
Main Authors: Archit Verma, Barbara E. Engelhardt
Format: Article
Language:English
Published: BMC 2020-07-01
Series:BMC Bioinformatics
Subjects:
Manifold learning
Single cell RNA sequencing
Gaussian process latent variable model
Dimension reduction
Robust model
Nonlinear maps
Online Access:http://link.springer.com/article/10.1186/s12859-020-03625-z

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http://link.springer.com/article/10.1186/s12859-020-03625-z

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