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ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single‐cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by...

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Detalles Bibliográficos
Autores principales: Zhang, Ying, Sun, Huaicheng, Lian, Xichen, Tang, Jing, Zhu, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214264/
https://www.ncbi.nlm.nih.gov/pubmed/36950745
http://dx.doi.org/10.1002/advs.202207061
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author Zhang, Ying
Sun, Huaicheng
Lian, Xichen
Tang, Jing
Zhu, Feng
author_facet Zhang, Ying
Sun, Huaicheng
Lian, Xichen
Tang, Jing
Zhu, Feng
author_sort Zhang, Ying
collection PubMed
description ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single‐cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in‐depth and high‐quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well‐performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.
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spelling pubmed-102142642023-05-27 ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics Zhang, Ying Sun, Huaicheng Lian, Xichen Tang, Jing Zhu, Feng Adv Sci (Weinh) Research Articles ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single‐cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in‐depth and high‐quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well‐performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights. John Wiley and Sons Inc. 2023-03-22 /pmc/articles/PMC10214264/ /pubmed/36950745 http://dx.doi.org/10.1002/advs.202207061 Text en © 2023 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Zhang, Ying
Sun, Huaicheng
Lian, Xichen
Tang, Jing
Zhu, Feng
ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics
title ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics
title_full ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics
title_fullStr ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics
title_full_unstemmed ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics
title_short ANPELA: Significantly Enhanced Quantification Tool for Cytometry‐Based Single‐Cell Proteomics
title_sort anpela: significantly enhanced quantification tool for cytometry‐based single‐cell proteomics
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214264/
https://www.ncbi.nlm.nih.gov/pubmed/36950745
http://dx.doi.org/10.1002/advs.202207061
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