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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...

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Autores principales: Hwang, Hyun, Liu, Rui, Maxwell, Joshua T., Yang, Jingjing, Xu, Chunhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
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.
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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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