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MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets
Large collections of annotated single-cell RNA sequencing (scRNA-seq) experiments are being generated across different organs, conditions and organisms on different platforms. The diversity, volume and complexity of this aggregated data requires new analysis techniques to extract actionable knowledg...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8593221/ https://www.ncbi.nlm.nih.gov/pubmed/34796179 http://dx.doi.org/10.3389/fcell.2021.767897 |
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author | Koh, Winston Hoon, Shawn |
author_facet | Koh, Winston Hoon, Shawn |
author_sort | Koh, Winston |
collection | PubMed |
description | Large collections of annotated single-cell RNA sequencing (scRNA-seq) experiments are being generated across different organs, conditions and organisms on different platforms. The diversity, volume and complexity of this aggregated data requires new analysis techniques to extract actionable knowledge. Fundamental to most analysis are key abilities such as: identification of similar cells across different experiments and transferring annotations from an annotated dataset to an unannotated one. There have been many strategies explored in achieving these goals, and they focuses primarily on aligning and re-clustering datasets of interest. In this work, we are interested in exploring the applicability of deep metric learning methods as a form of distance function to capture similarity between cells and facilitate the transfer of cell type annotation for similar cells across different experiments. Toward this aim, we developed MapCell, a few-shot training approach using Siamese Neural Networks (SNNs) to learn a generalizable distance metric that can differentiate between single cell types. Requiring only a small training set, we demonstrated that SNN derived distance metric can perform accurate transfer of annotation across different scRNA-seq platforms, batches, species and also aid in flagging novel cell types. |
format | Online Article Text |
id | pubmed-8593221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85932212021-11-17 MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets Koh, Winston Hoon, Shawn Front Cell Dev Biol Cell and Developmental Biology Large collections of annotated single-cell RNA sequencing (scRNA-seq) experiments are being generated across different organs, conditions and organisms on different platforms. The diversity, volume and complexity of this aggregated data requires new analysis techniques to extract actionable knowledge. Fundamental to most analysis are key abilities such as: identification of similar cells across different experiments and transferring annotations from an annotated dataset to an unannotated one. There have been many strategies explored in achieving these goals, and they focuses primarily on aligning and re-clustering datasets of interest. In this work, we are interested in exploring the applicability of deep metric learning methods as a form of distance function to capture similarity between cells and facilitate the transfer of cell type annotation for similar cells across different experiments. Toward this aim, we developed MapCell, a few-shot training approach using Siamese Neural Networks (SNNs) to learn a generalizable distance metric that can differentiate between single cell types. Requiring only a small training set, we demonstrated that SNN derived distance metric can perform accurate transfer of annotation across different scRNA-seq platforms, batches, species and also aid in flagging novel cell types. Frontiers Media S.A. 2021-11-02 /pmc/articles/PMC8593221/ /pubmed/34796179 http://dx.doi.org/10.3389/fcell.2021.767897 Text en Copyright © 2021 Koh and Hoon. https://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 | Cell and Developmental Biology Koh, Winston Hoon, Shawn MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets |
title | MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets |
title_full | MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets |
title_fullStr | MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets |
title_full_unstemmed | MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets |
title_short | MapCell: Learning a Comparative Cell Type Distance Metric With Siamese Neural Nets With Applications Toward Cell-Type Identification Across Experimental Datasets |
title_sort | mapcell: learning a comparative cell type distance metric with siamese neural nets with applications toward cell-type identification across experimental datasets |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8593221/ https://www.ncbi.nlm.nih.gov/pubmed/34796179 http://dx.doi.org/10.3389/fcell.2021.767897 |
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