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Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis
Although single-cell RNA sequencing (scRNA-seq) technology is newly invented and a promising one, but because of lack of enough information that labels individual cells, it is hard to interpret the obtained gene expression of each cell. Because of insufficient information available, unsupervised clu...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6761323/ https://www.ncbi.nlm.nih.gov/pubmed/31608111 http://dx.doi.org/10.3389/fgene.2019.00864 |
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author | Taguchi, Y-h. Turki, Turki |
author_facet | Taguchi, Y-h. Turki, Turki |
author_sort | Taguchi, Y-h. |
collection | PubMed |
description | Although single-cell RNA sequencing (scRNA-seq) technology is newly invented and a promising one, but because of lack of enough information that labels individual cells, it is hard to interpret the obtained gene expression of each cell. Because of insufficient information available, unsupervised clustering, for example, t-distributed stochastic neighbor embedding and uniform manifold approximation and projection, is usually employed to obtain low-dimensional embedding that can help to understand cell–cell relationship. One possible drawback of this strategy is that the outcome is highly dependent upon genes selected for the usage of clustering. In order to fulfill this requirement, there are many methods that performed unsupervised gene selection. In this study, a tensor decomposition (TD)-based unsupervised feature extraction (FE) was applied to the integration of two scRNA-seq expression profiles that measure human and mouse midbrain development. TD-based unsupervised FE could select not only coincident genes between human and mouse but also biologically reliable genes. Coincidence between two species as well as biological reliability of selected genes is increased compared with that using principal component analysis (PCA)-based FE applied to the same data set in the previous study. Since PCA-based unsupervised FE outperformed the other three popular unsupervised gene selection methods, highly variable genes, bimodal genes, and dpFeature, TD-based unsupervised FE can do so as well. In addition to this, 10 transcription factors (TFs) that might regulate selected genes and might contribute to midbrain development were identified. These 10 TFs, BHLHE40, EGR1, GABPA, IRF3, PPARG, REST, RFX5, STAT3, TCF7L2, and ZBTB33, were previously reported to be related to brain functions and diseases. TD-based unsupervised FE is a promising method to integrate two scRNA-seq profiles effectively. |
format | Online Article Text |
id | pubmed-6761323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67613232019-10-13 Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis Taguchi, Y-h. Turki, Turki Front Genet Genetics Although single-cell RNA sequencing (scRNA-seq) technology is newly invented and a promising one, but because of lack of enough information that labels individual cells, it is hard to interpret the obtained gene expression of each cell. Because of insufficient information available, unsupervised clustering, for example, t-distributed stochastic neighbor embedding and uniform manifold approximation and projection, is usually employed to obtain low-dimensional embedding that can help to understand cell–cell relationship. One possible drawback of this strategy is that the outcome is highly dependent upon genes selected for the usage of clustering. In order to fulfill this requirement, there are many methods that performed unsupervised gene selection. In this study, a tensor decomposition (TD)-based unsupervised feature extraction (FE) was applied to the integration of two scRNA-seq expression profiles that measure human and mouse midbrain development. TD-based unsupervised FE could select not only coincident genes between human and mouse but also biologically reliable genes. Coincidence between two species as well as biological reliability of selected genes is increased compared with that using principal component analysis (PCA)-based FE applied to the same data set in the previous study. Since PCA-based unsupervised FE outperformed the other three popular unsupervised gene selection methods, highly variable genes, bimodal genes, and dpFeature, TD-based unsupervised FE can do so as well. In addition to this, 10 transcription factors (TFs) that might regulate selected genes and might contribute to midbrain development were identified. These 10 TFs, BHLHE40, EGR1, GABPA, IRF3, PPARG, REST, RFX5, STAT3, TCF7L2, and ZBTB33, were previously reported to be related to brain functions and diseases. TD-based unsupervised FE is a promising method to integrate two scRNA-seq profiles effectively. Frontiers Media S.A. 2019-09-19 /pmc/articles/PMC6761323/ /pubmed/31608111 http://dx.doi.org/10.3389/fgene.2019.00864 Text en Copyright © 2019 Taguchi and Turki http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Taguchi, Y-h. Turki, Turki Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis |
title | Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis |
title_full | Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis |
title_fullStr | Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis |
title_full_unstemmed | Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis |
title_short | Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis |
title_sort | tensor decomposition-based unsupervised feature extraction applied to single-cell gene expression analysis |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6761323/ https://www.ncbi.nlm.nih.gov/pubmed/31608111 http://dx.doi.org/10.3389/fgene.2019.00864 |
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