TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer
Recent advances in single-cell RNA sequencing (scRNA-seq) have accelerated the development of techniques to classify thousands of cells through transcriptome profiling. As more and more scRNA-seq data become available, supervised cell type classification methods using externally well-annotated sourc...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.1038919/full |
| _version_ | 1811241841633263616 |
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| author | Tao Song Tao Song Huanhuan Dai Shuang Wang Gan Wang Xudong Zhang Ying Zhang Linfang Jiao |
| author_facet | Tao Song Tao Song Huanhuan Dai Shuang Wang Gan Wang Xudong Zhang Ying Zhang Linfang Jiao |
| author_sort | Tao Song |
| collection | DOAJ |
| description | Recent advances in single-cell RNA sequencing (scRNA-seq) have accelerated the development of techniques to classify thousands of cells through transcriptome profiling. As more and more scRNA-seq data become available, supervised cell type classification methods using externally well-annotated source data become more popular than unsupervised clustering algorithms. However, accurate cellular annotation of single cell transcription data remains a significant challenge. Here, we propose a hybrid network structure called TransCluster, which uses linear discriminant analysis and a modified Transformer to enhance feature learning. It is a cell-type identification tool for single-cell transcriptomic maps. It shows high accuracy and robustness in many cell data sets of different human tissues. It is superior to other known methods in external test data set. To our knowledge, TransCluster is the first attempt to use Transformer for annotating cell types of scRNA-seq, which greatly improves the accuracy of cell-type identification. |
| first_indexed | 2024-04-12T13:42:06Z |
| format | Article |
| id | doaj.art-95ca4b57d467444ab1ae35614360002b |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-12T13:42:06Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-95ca4b57d467444ab1ae35614360002b2022-12-22T03:30:49ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-10-011310.3389/fgene.2022.10389191038919TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformerTao Song0Tao Song1Huanhuan Dai2Shuang Wang3Gan Wang4Xudong Zhang5Ying Zhang6Linfang Jiao7College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, ChinaDepartment of Artificial Intelligence, Faculty of Computer Science, Campus de Montegancedo, Polytechnical University of Madrid, Boadilla Del Monte, Madrid, SpainCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao, ChinaRecent advances in single-cell RNA sequencing (scRNA-seq) have accelerated the development of techniques to classify thousands of cells through transcriptome profiling. As more and more scRNA-seq data become available, supervised cell type classification methods using externally well-annotated source data become more popular than unsupervised clustering algorithms. However, accurate cellular annotation of single cell transcription data remains a significant challenge. Here, we propose a hybrid network structure called TransCluster, which uses linear discriminant analysis and a modified Transformer to enhance feature learning. It is a cell-type identification tool for single-cell transcriptomic maps. It shows high accuracy and robustness in many cell data sets of different human tissues. It is superior to other known methods in external test data set. To our knowledge, TransCluster is the first attempt to use Transformer for annotating cell types of scRNA-seq, which greatly improves the accuracy of cell-type identification.https://www.frontiersin.org/articles/10.3389/fgene.2022.1038919/fullcell-type identificationsingle-cell sequencing datatransformerneural networkdeep learning |
| spellingShingle | Tao Song Tao Song Huanhuan Dai Shuang Wang Gan Wang Xudong Zhang Ying Zhang Linfang Jiao TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer Frontiers in Genetics cell-type identification single-cell sequencing data transformer neural network deep learning |
| title | TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer |
| title_full | TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer |
| title_fullStr | TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer |
| title_full_unstemmed | TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer |
| title_short | TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer |
| title_sort | transcluster a cell type identification method for single cell rna seq data using deep learning based on transformer |
| topic | cell-type identification single-cell sequencing data transformer neural network deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.1038919/full |
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