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Machine learning identifies abnormal Ca(2+)transients in human induced pluripotent stem cell-derived cardiomyocytes
Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca(2+) transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we de...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550597/ https://www.ncbi.nlm.nih.gov/pubmed/33046816 http://dx.doi.org/10.1038/s41598-020-73801-x |
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author | Hwang, Hyun Liu, Rui Maxwell, Joshua T. Yang, Jingjing Xu, Chunhui |
author_facet | Hwang, Hyun Liu, Rui Maxwell, Joshua T. Yang, Jingjing Xu, Chunhui |
author_sort | Hwang, Hyun |
collection | PubMed |
description | Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca(2+) transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca(2+) transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca(2+) transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profiled peak variables as predictive features. Third, we train another cell-level SVM classifier by using human-expert assessment of cell abnormality as outcome and quantified cell-level variables as predictive features. This cell-level SVM classifier can be used to assess additional Ca(2+) transient signals. By applying this pipeline to our Ca(2+) transient data, we trained a cell-level SVM classifier using 200 cells as training data, then tested its accuracy in an independent dataset of 54 cells. As a result, we obtained 88% training accuracy and 87% test accuracy. Further, we provide a free R package to implement our pipeline for high-throughput CM Ca(2+) analysis. |
format | Online Article Text |
id | pubmed-7550597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75505972020-10-14 Machine learning identifies abnormal Ca(2+)transients in human induced pluripotent stem cell-derived cardiomyocytes Hwang, Hyun Liu, Rui Maxwell, Joshua T. Yang, Jingjing Xu, Chunhui Sci Rep Article Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca(2+) transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca(2+) transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca(2+) transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profiled peak variables as predictive features. Third, we train another cell-level SVM classifier by using human-expert assessment of cell abnormality as outcome and quantified cell-level variables as predictive features. This cell-level SVM classifier can be used to assess additional Ca(2+) transient signals. By applying this pipeline to our Ca(2+) transient data, we trained a cell-level SVM classifier using 200 cells as training data, then tested its accuracy in an independent dataset of 54 cells. As a result, we obtained 88% training accuracy and 87% test accuracy. Further, we provide a free R package to implement our pipeline for high-throughput CM Ca(2+) analysis. Nature Publishing Group UK 2020-10-12 /pmc/articles/PMC7550597/ /pubmed/33046816 http://dx.doi.org/10.1038/s41598-020-73801-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Hwang, Hyun Liu, Rui Maxwell, Joshua T. Yang, Jingjing Xu, Chunhui Machine learning identifies abnormal Ca(2+)transients in human induced pluripotent stem cell-derived cardiomyocytes |
title | Machine learning identifies abnormal Ca(2+)transients in human induced pluripotent stem cell-derived cardiomyocytes |
title_full | Machine learning identifies abnormal Ca(2+)transients in human induced pluripotent stem cell-derived cardiomyocytes |
title_fullStr | Machine learning identifies abnormal Ca(2+)transients in human induced pluripotent stem cell-derived cardiomyocytes |
title_full_unstemmed | Machine learning identifies abnormal Ca(2+)transients in human induced pluripotent stem cell-derived cardiomyocytes |
title_short | Machine learning identifies abnormal Ca(2+)transients in human induced pluripotent stem cell-derived cardiomyocytes |
title_sort | machine learning identifies abnormal ca(2+)transients in human induced pluripotent stem cell-derived cardiomyocytes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550597/ https://www.ncbi.nlm.nih.gov/pubmed/33046816 http://dx.doi.org/10.1038/s41598-020-73801-x |
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