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Computational Methods for Single-Cell Proteomics

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize...

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Detalles Bibliográficos
Autores principales: Guldberg, Sophia M., Okholm, Trine Line Hauge, McCarthy, Elizabeth E., Spitzer, Matthew H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621466/
https://www.ncbi.nlm.nih.gov/pubmed/37040735
http://dx.doi.org/10.1146/annurev-biodatasci-020422-050255
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author Guldberg, Sophia M.
Okholm, Trine Line Hauge
McCarthy, Elizabeth E.
Spitzer, Matthew H.
author_facet Guldberg, Sophia M.
Okholm, Trine Line Hauge
McCarthy, Elizabeth E.
Spitzer, Matthew H.
author_sort Guldberg, Sophia M.
collection PubMed
description Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.
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spelling pubmed-106214662023-11-02 Computational Methods for Single-Cell Proteomics Guldberg, Sophia M. Okholm, Trine Line Hauge McCarthy, Elizabeth E. Spitzer, Matthew H. Annu Rev Biomed Data Sci Article Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data. 2023-08-10 2023-04-11 /pmc/articles/PMC10621466/ /pubmed/37040735 http://dx.doi.org/10.1146/annurev-biodatasci-020422-050255 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.
spellingShingle Article
Guldberg, Sophia M.
Okholm, Trine Line Hauge
McCarthy, Elizabeth E.
Spitzer, Matthew H.
Computational Methods for Single-Cell Proteomics
title Computational Methods for Single-Cell Proteomics
title_full Computational Methods for Single-Cell Proteomics
title_fullStr Computational Methods for Single-Cell Proteomics
title_full_unstemmed Computational Methods for Single-Cell Proteomics
title_short Computational Methods for Single-Cell Proteomics
title_sort computational methods for single-cell proteomics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621466/
https://www.ncbi.nlm.nih.gov/pubmed/37040735
http://dx.doi.org/10.1146/annurev-biodatasci-020422-050255
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