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Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization
Nonlinear dimensionality reduction (NLDR) methods such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used for biological data exploration, especially in single-cell analysis. However, the existing methods have drawba...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316349/ https://www.ncbi.nlm.nih.gov/pubmed/35887125 http://dx.doi.org/10.3390/ijms23147775 |
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author | Wang, Yajuan Xu, Yongjie Zang, Zelin Wu, Lirong Li, Ziqing |
author_facet | Wang, Yajuan Xu, Yongjie Zang, Zelin Wu, Lirong Li, Ziqing |
author_sort | Wang, Yajuan |
collection | PubMed |
description | Nonlinear dimensionality reduction (NLDR) methods such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used for biological data exploration, especially in single-cell analysis. However, the existing methods have drawbacks in preserving data’s geometric and topological structures. A high-dimensional data analysis method, called Panoramic manifold projection (Panoramap), was developed as an enhanced deep learning framework for structure-preserving NLDR. Panoramap enhances deep neural networks by using cross-layer geometry-preserving constraints. The constraints constitute the loss for deep manifold learning and serve as geometric regularizers for NLDR network training. Therefore, Panoramap has better performance in preserving global structures of the original data. Here, we apply Panoramap to single-cell datasets and show that Panoramap excels at delineating the cell type lineage/hierarchy and can reveal rare cell types. Panoramap can facilitate trajectory inference and has the potential to aid in the early diagnosis of tumors. Panoramap gives improved and more biologically plausible visualization and interpretation of single-cell data. Panoramap can be readily used in single-cell research domains and other research fields that involve high dimensional data analysis. |
format | Online Article Text |
id | pubmed-9316349 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93163492022-07-27 Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization Wang, Yajuan Xu, Yongjie Zang, Zelin Wu, Lirong Li, Ziqing Int J Mol Sci Article Nonlinear dimensionality reduction (NLDR) methods such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used for biological data exploration, especially in single-cell analysis. However, the existing methods have drawbacks in preserving data’s geometric and topological structures. A high-dimensional data analysis method, called Panoramic manifold projection (Panoramap), was developed as an enhanced deep learning framework for structure-preserving NLDR. Panoramap enhances deep neural networks by using cross-layer geometry-preserving constraints. The constraints constitute the loss for deep manifold learning and serve as geometric regularizers for NLDR network training. Therefore, Panoramap has better performance in preserving global structures of the original data. Here, we apply Panoramap to single-cell datasets and show that Panoramap excels at delineating the cell type lineage/hierarchy and can reveal rare cell types. Panoramap can facilitate trajectory inference and has the potential to aid in the early diagnosis of tumors. Panoramap gives improved and more biologically plausible visualization and interpretation of single-cell data. Panoramap can be readily used in single-cell research domains and other research fields that involve high dimensional data analysis. MDPI 2022-07-14 /pmc/articles/PMC9316349/ /pubmed/35887125 http://dx.doi.org/10.3390/ijms23147775 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Yajuan Xu, Yongjie Zang, Zelin Wu, Lirong Li, Ziqing Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization |
title | Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization |
title_full | Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization |
title_fullStr | Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization |
title_full_unstemmed | Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization |
title_short | Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization |
title_sort | panoramic manifold projection (panoramap) for single-cell data dimensionality reduction and visualization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316349/ https://www.ncbi.nlm.nih.gov/pubmed/35887125 http://dx.doi.org/10.3390/ijms23147775 |
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