A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data

Motivation Single-cell RNA sequencing (scRNA-seq) has been widely used to decompose complex tissues into functionally distinct cell types. The first and usually the most important step of scRNA-seq data analysis is to accurately annotate the cell labels. In recent years, many supervised annotation m...

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Autores: Li, Z. (Ziyi)|||/items/a41ceaa3-6dda-4802-8d3c-8d637fcf615c, Wang, Y. (Yizhuo)|||/items/3b7897a9-85d6-42b7-872b-dd16d19c89e6, Gañán-Gómez, I. (Irene)|||/items/92d1787d-8a7f-414a-af8a-a0cc2094a8af, Colla, S. (Simona)|||/items/659f9e93-5695-4c4a-948e-85565d696acc, Do, K.A. (Kim Anh)|||/items/aa77554a-97de-45b5-a9ed-f3c00a8841f8
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Navarra
Repositorio:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglés
OAI Identifier:oai:dadun.unav.edu:10171/121455
Acceso en línea:https://hdl.handle.net/10171/121455
Access Level:acceso abierto
Palabra clave:Single-cell RNA sequencing (scRNA-seq)
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spelling A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq dataLi, Z. (Ziyi)|||/items/a41ceaa3-6dda-4802-8d3c-8d637fcf615cWang, Y. (Yizhuo)|||/items/3b7897a9-85d6-42b7-872b-dd16d19c89e6Gañán-Gómez, I. (Irene)|||/items/92d1787d-8a7f-414a-af8a-a0cc2094a8afColla, S. (Simona)|||/items/659f9e93-5695-4c4a-948e-85565d696accDo, K.A. (Kim Anh)|||/items/aa77554a-97de-45b5-a9ed-f3c00a8841f8Single-cell RNA sequencing (scRNA-seq)Motivation Single-cell RNA sequencing (scRNA-seq) has been widely used to decompose complex tissues into functionally distinct cell types. The first and usually the most important step of scRNA-seq data analysis is to accurately annotate the cell labels. In recent years, many supervised annotation methods have been developed and shown to be more convenient and accurate than unsupervised cell clustering. One challenge faced by all the supervised annotation methods is the identification of the novel cell type, which is defined as the cell type that is not present in the training data, only exists in the testing data. Existing methods usually label the cells simply based on the correlation coefficients or confidence scores, which sometimes results in an excessive number of unlabeled cells. Results We developed a straightforward yet effective method combining autoencoder with iterative feature selection to automatically identify novel cells from scRNA-seq data. Our method trains an autoencoder with the labeled training data and applies the autoencoder to the testing data to obtain reconstruction errors. By iteratively selecting features that demonstrate a bi-modal pattern and reclustering the cells using the selected feature, our method can accurately identify novel cells that are not present in the training data. We further combined this approach with a support vector machine to provide a complete solution for annotating the full range of cell types. Extensive numerical experiments using five real scRNA-seq datasets demonstrated favorable performance of the proposed method over existing methods serving similar purposes.Oxford AcademicDadun. Depósito Académico Digital Universidad de Navarra20222022-01-0120222022-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10171/121455reponame:Dadun. Depósito Académico Digital de la Universidad de Navarrainstname:Universidad de NavarraInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dadun.unav.edu:10171/1214552026-06-21T12:47:57Z
dc.title.none.fl_str_mv A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data
title A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data
spellingShingle A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data
Li, Z. (Ziyi)|||/items/a41ceaa3-6dda-4802-8d3c-8d637fcf615c
Single-cell RNA sequencing (scRNA-seq)
title_short A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data
title_full A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data
title_fullStr A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data
title_full_unstemmed A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data
title_sort A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data
dc.creator.none.fl_str_mv Li, Z. (Ziyi)|||/items/a41ceaa3-6dda-4802-8d3c-8d637fcf615c
Wang, Y. (Yizhuo)|||/items/3b7897a9-85d6-42b7-872b-dd16d19c89e6
Gañán-Gómez, I. (Irene)|||/items/92d1787d-8a7f-414a-af8a-a0cc2094a8af
Colla, S. (Simona)|||/items/659f9e93-5695-4c4a-948e-85565d696acc
Do, K.A. (Kim Anh)|||/items/aa77554a-97de-45b5-a9ed-f3c00a8841f8
author Li, Z. (Ziyi)|||/items/a41ceaa3-6dda-4802-8d3c-8d637fcf615c
author_facet Li, Z. (Ziyi)|||/items/a41ceaa3-6dda-4802-8d3c-8d637fcf615c
Wang, Y. (Yizhuo)|||/items/3b7897a9-85d6-42b7-872b-dd16d19c89e6
Gañán-Gómez, I. (Irene)|||/items/92d1787d-8a7f-414a-af8a-a0cc2094a8af
Colla, S. (Simona)|||/items/659f9e93-5695-4c4a-948e-85565d696acc
Do, K.A. (Kim Anh)|||/items/aa77554a-97de-45b5-a9ed-f3c00a8841f8
author_role author
author2 Wang, Y. (Yizhuo)|||/items/3b7897a9-85d6-42b7-872b-dd16d19c89e6
Gañán-Gómez, I. (Irene)|||/items/92d1787d-8a7f-414a-af8a-a0cc2094a8af
Colla, S. (Simona)|||/items/659f9e93-5695-4c4a-948e-85565d696acc
Do, K.A. (Kim Anh)|||/items/aa77554a-97de-45b5-a9ed-f3c00a8841f8
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Dadun. Depósito Académico Digital Universidad de Navarra
dc.subject.none.fl_str_mv Single-cell RNA sequencing (scRNA-seq)
topic Single-cell RNA sequencing (scRNA-seq)
description Motivation Single-cell RNA sequencing (scRNA-seq) has been widely used to decompose complex tissues into functionally distinct cell types. The first and usually the most important step of scRNA-seq data analysis is to accurately annotate the cell labels. In recent years, many supervised annotation methods have been developed and shown to be more convenient and accurate than unsupervised cell clustering. One challenge faced by all the supervised annotation methods is the identification of the novel cell type, which is defined as the cell type that is not present in the training data, only exists in the testing data. Existing methods usually label the cells simply based on the correlation coefficients or confidence scores, which sometimes results in an excessive number of unlabeled cells. Results We developed a straightforward yet effective method combining autoencoder with iterative feature selection to automatically identify novel cells from scRNA-seq data. Our method trains an autoencoder with the labeled training data and applies the autoencoder to the testing data to obtain reconstruction errors. By iteratively selecting features that demonstrate a bi-modal pattern and reclustering the cells using the selected feature, our method can accurately identify novel cells that are not present in the training data. We further combined this approach with a support vector machine to provide a complete solution for annotating the full range of cell types. Extensive numerical experiments using five real scRNA-seq datasets demonstrated favorable performance of the proposed method over existing methods serving similar purposes.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01
2022
2022-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10171/121455
url https://hdl.handle.net/10171/121455
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Oxford Academic
publisher.none.fl_str_mv Oxford Academic
dc.source.none.fl_str_mv reponame:Dadun. Depósito Académico Digital de la Universidad de Navarra
instname:Universidad de Navarra
instname_str Universidad de Navarra
reponame_str Dadun. Depósito Académico Digital de la Universidad de Navarra
collection Dadun. Depósito Académico Digital de la Universidad de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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