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Graph of graphs analysis for multiplexed data with application to imaging mass cytometry

Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a sp...

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Autores principales: Lin, Ya-Wei Eileen, Shnitzer, Tal, Talmon, Ronen, Villarroel-Espindola, Franz, Desai, Shruti, Schalper, Kurt, Kluger, Yuval
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032202/
https://www.ncbi.nlm.nih.gov/pubmed/33780435
http://dx.doi.org/10.1371/journal.pcbi.1008741
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author Lin, Ya-Wei Eileen
Shnitzer, Tal
Talmon, Ronen
Villarroel-Espindola, Franz
Desai, Shruti
Schalper, Kurt
Kluger, Yuval
author_facet Lin, Ya-Wei Eileen
Shnitzer, Tal
Talmon, Ronen
Villarroel-Espindola, Franz
Desai, Shruti
Schalper, Kurt
Kluger, Yuval
author_sort Lin, Ya-Wei Eileen
collection PubMed
description Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image.
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spelling pubmed-80322022021-04-15 Graph of graphs analysis for multiplexed data with application to imaging mass cytometry Lin, Ya-Wei Eileen Shnitzer, Tal Talmon, Ronen Villarroel-Espindola, Franz Desai, Shruti Schalper, Kurt Kluger, Yuval PLoS Comput Biol Research Article Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image. Public Library of Science 2021-03-29 /pmc/articles/PMC8032202/ /pubmed/33780435 http://dx.doi.org/10.1371/journal.pcbi.1008741 Text en © 2021 Lin et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lin, Ya-Wei Eileen
Shnitzer, Tal
Talmon, Ronen
Villarroel-Espindola, Franz
Desai, Shruti
Schalper, Kurt
Kluger, Yuval
Graph of graphs analysis for multiplexed data with application to imaging mass cytometry
title Graph of graphs analysis for multiplexed data with application to imaging mass cytometry
title_full Graph of graphs analysis for multiplexed data with application to imaging mass cytometry
title_fullStr Graph of graphs analysis for multiplexed data with application to imaging mass cytometry
title_full_unstemmed Graph of graphs analysis for multiplexed data with application to imaging mass cytometry
title_short Graph of graphs analysis for multiplexed data with application to imaging mass cytometry
title_sort graph of graphs analysis for multiplexed data with application to imaging mass cytometry
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032202/
https://www.ncbi.nlm.nih.gov/pubmed/33780435
http://dx.doi.org/10.1371/journal.pcbi.1008741
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