Cell-type annotation with accurate unseen cell-type identification using multiple references.
The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becom...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011261&type=printable |
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| author | Yi-Xuan Xiong Meng-Guo Wang Luonan Chen Xiao-Fei Zhang |
| author_facet | Yi-Xuan Xiong Meng-Guo Wang Luonan Chen Xiao-Fei Zhang |
| author_sort | Yi-Xuan Xiong |
| collection | DOAJ |
| description | The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques. Identifying previously unseen cell types is essential for improving annotation accuracy and uncovering novel biological discoveries. To address this challenge, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the aid of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric that considers three complementary aspects to distinguish between unseen cell types and shared cell types. Additionally, we provide a data-driven method to adaptively select a threshold for identifying previously unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for unseen cell-type identification and cell-type annotation on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets. The source code and tutorial are available at https://github.com/Zhangxf-ccnu/mtANN. |
| format | Article |
| id | doaj-art-eca29efb6bd246be810c85f9d22ef756 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-eca29efb6bd246be810c85f9d22ef7562025-08-20T03:57:59ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-06-01196e101126110.1371/journal.pcbi.1011261Cell-type annotation with accurate unseen cell-type identification using multiple references.Yi-Xuan XiongMeng-Guo WangLuonan ChenXiao-Fei ZhangThe recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques. Identifying previously unseen cell types is essential for improving annotation accuracy and uncovering novel biological discoveries. To address this challenge, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the aid of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric that considers three complementary aspects to distinguish between unseen cell types and shared cell types. Additionally, we provide a data-driven method to adaptively select a threshold for identifying previously unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for unseen cell-type identification and cell-type annotation on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets. The source code and tutorial are available at https://github.com/Zhangxf-ccnu/mtANN.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011261&type=printable |
| spellingShingle | Yi-Xuan Xiong Meng-Guo Wang Luonan Chen Xiao-Fei Zhang Cell-type annotation with accurate unseen cell-type identification using multiple references. PLoS Computational Biology |
| title | Cell-type annotation with accurate unseen cell-type identification using multiple references. |
| title_full | Cell-type annotation with accurate unseen cell-type identification using multiple references. |
| title_fullStr | Cell-type annotation with accurate unseen cell-type identification using multiple references. |
| title_full_unstemmed | Cell-type annotation with accurate unseen cell-type identification using multiple references. |
| title_short | Cell-type annotation with accurate unseen cell-type identification using multiple references. |
| title_sort | cell type annotation with accurate unseen cell type identification using multiple references |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011261&type=printable |
| work_keys_str_mv | AT yixuanxiong celltypeannotationwithaccurateunseencelltypeidentificationusingmultiplereferences AT mengguowang celltypeannotationwithaccurateunseencelltypeidentificationusingmultiplereferences AT luonanchen celltypeannotationwithaccurateunseencelltypeidentificationusingmultiplereferences AT xiaofeizhang celltypeannotationwithaccurateunseencelltypeidentificationusingmultiplereferences |