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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doaj-90e627a3b7bb41bb90a6c2efc6e5c8342020-11-25T03:54:04ZengBMCBMC Bioinformatics1471-21052020-07-0121111510.1186/s12859-020-03625-zA robust nonlinear low-dimensional manifold for single cell RNA-seq dataArchit Verma0Barbara E. Engelhardt1Chemical and Biological Engineering, Princeton UniversityComputer Science, Center for Statistics and Machine LearningAbstract 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 and organs. Analysis of these sparse, high-dimensional experimental results requires dimension reduction. Several methods have been developed to estimate low-dimensional embeddings for filtered and normalized single-cell data. However, methods have yet to be developed for unfiltered and unnormalized count data that estimate uncertainty in the low-dimensional space. We present a nonlinear latent variable model with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensional nonlinear structure in scRNA-seq data. Results Gene expression in a single cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional latent positions. This model is called the Gaussian process latent variable model (GPLVM). We model residual errors with a heavy-tailed Student’s t-distribution to estimate a manifold that is robust to technical and biological noise found in normalized scRNA-seq data. We compare our approach to common dimension reduction tools across a diverse set of scRNA-seq data sets to highlight our model’s ability to enable important downstream tasks such as clustering, inferring cell developmental trajectories, and visualizing high throughput experiments on available experimental data. Conclusion We show that our adaptive robust statistical approach to estimate a nonlinear manifold is well suited for raw, unfiltered gene counts from high-throughput sequencing technologies for visualization, exploration, and uncertainty estimation of cell states.http://link.springer.com/article/10.1186/s12859-020-03625-zManifold learningSingle cell RNA sequencingGaussian process latent variable modelDimension reductionRobust modelNonlinear maps |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Archit Verma Barbara E. Engelhardt |
spellingShingle |
Archit Verma Barbara E. Engelhardt A robust nonlinear low-dimensional manifold for single cell RNA-seq data BMC Bioinformatics Manifold learning Single cell RNA sequencing Gaussian process latent variable model Dimension reduction Robust model Nonlinear maps |
author_facet |
Archit Verma Barbara E. Engelhardt |
author_sort |
Archit Verma |
title |
A robust nonlinear low-dimensional manifold for single cell RNA-seq data |
title_short |
A robust nonlinear low-dimensional manifold for single cell RNA-seq data |
title_full |
A robust nonlinear low-dimensional manifold for single cell RNA-seq data |
title_fullStr |
A robust nonlinear low-dimensional manifold for single cell RNA-seq data |
title_full_unstemmed |
A robust nonlinear low-dimensional manifold for single cell RNA-seq data |
title_sort |
robust nonlinear low-dimensional manifold for single cell rna-seq data |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2020-07-01 |
description |
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 and organs. Analysis of these sparse, high-dimensional experimental results requires dimension reduction. Several methods have been developed to estimate low-dimensional embeddings for filtered and normalized single-cell data. However, methods have yet to be developed for unfiltered and unnormalized count data that estimate uncertainty in the low-dimensional space. We present a nonlinear latent variable model with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensional nonlinear structure in scRNA-seq data. Results Gene expression in a single cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional latent positions. This model is called the Gaussian process latent variable model (GPLVM). We model residual errors with a heavy-tailed Student’s t-distribution to estimate a manifold that is robust to technical and biological noise found in normalized scRNA-seq data. We compare our approach to common dimension reduction tools across a diverse set of scRNA-seq data sets to highlight our model’s ability to enable important downstream tasks such as clustering, inferring cell developmental trajectories, and visualizing high throughput experiments on available experimental data. Conclusion We show that our adaptive robust statistical approach to estimate a nonlinear manifold is well suited for raw, unfiltered gene counts from high-throughput sequencing technologies for visualization, exploration, and uncertainty estimation of cell states. |
topic |
Manifold learning Single cell RNA sequencing Gaussian process latent variable model Dimension reduction Robust model Nonlinear maps |
url |
http://link.springer.com/article/10.1186/s12859-020-03625-z |
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