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PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes

Relating expression signatures from different sources such as cell lines, in vitro cultures from primary cells and biopsy material is an important task in drug development and translational medicine as well as for tracking of cell fate and disease progression. Especially the comparison of large scal...

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Autores principales: Lenz, Michael, Schuldt, Bernhard M., Müller, Franz-Josef, Schuppert, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3798402/
https://www.ncbi.nlm.nih.gov/pubmed/24147039
http://dx.doi.org/10.1371/journal.pone.0077627
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author Lenz, Michael
Schuldt, Bernhard M.
Müller, Franz-Josef
Schuppert, Andreas
author_facet Lenz, Michael
Schuldt, Bernhard M.
Müller, Franz-Josef
Schuppert, Andreas
author_sort Lenz, Michael
collection PubMed
description Relating expression signatures from different sources such as cell lines, in vitro cultures from primary cells and biopsy material is an important task in drug development and translational medicine as well as for tracking of cell fate and disease progression. Especially the comparison of large scale gene expression changes to tissue or cell type specific signatures is of high interest for the tracking of cell fate in (trans-) differentiation experiments and for cancer research, which increasingly focuses on shared processes and the involvement of the microenvironment. These signature relation approaches require robust statistical methods to account for the high biological heterogeneity in clinical data and must cope with small sample sizes in lab experiments and common patterns of co-expression in ubiquitous cellular processes. We describe a novel method, called PhysioSpace, to position dynamics of time series data derived from cellular differentiation and disease progression in a genome-wide expression space. The PhysioSpace is defined by a compendium of publicly available gene expression signatures representing a large set of biological phenotypes. The mapping of gene expression changes onto the PhysioSpace leads to a robust ranking of physiologically relevant signatures, as rigorously evaluated via sample-label permutations. A spherical transformation of the data improves the performance, leading to stable results even in case of small sample sizes. Using PhysioSpace with clinical cancer datasets reveals that such data exhibits large heterogeneity in the number of significant signature associations. This behavior was closely associated with the classification endpoint and cancer type under consideration, indicating shared biological functionalities in disease associated processes. Even though the time series data of cell line differentiation exhibited responses in larger clusters covering several biologically related patterns, top scoring patterns were highly consistent with a priory known biological information and separated from the rest of response patterns.
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spelling pubmed-37984022013-10-21 PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes Lenz, Michael Schuldt, Bernhard M. Müller, Franz-Josef Schuppert, Andreas PLoS One Research Article Relating expression signatures from different sources such as cell lines, in vitro cultures from primary cells and biopsy material is an important task in drug development and translational medicine as well as for tracking of cell fate and disease progression. Especially the comparison of large scale gene expression changes to tissue or cell type specific signatures is of high interest for the tracking of cell fate in (trans-) differentiation experiments and for cancer research, which increasingly focuses on shared processes and the involvement of the microenvironment. These signature relation approaches require robust statistical methods to account for the high biological heterogeneity in clinical data and must cope with small sample sizes in lab experiments and common patterns of co-expression in ubiquitous cellular processes. We describe a novel method, called PhysioSpace, to position dynamics of time series data derived from cellular differentiation and disease progression in a genome-wide expression space. The PhysioSpace is defined by a compendium of publicly available gene expression signatures representing a large set of biological phenotypes. The mapping of gene expression changes onto the PhysioSpace leads to a robust ranking of physiologically relevant signatures, as rigorously evaluated via sample-label permutations. A spherical transformation of the data improves the performance, leading to stable results even in case of small sample sizes. Using PhysioSpace with clinical cancer datasets reveals that such data exhibits large heterogeneity in the number of significant signature associations. This behavior was closely associated with the classification endpoint and cancer type under consideration, indicating shared biological functionalities in disease associated processes. Even though the time series data of cell line differentiation exhibited responses in larger clusters covering several biologically related patterns, top scoring patterns were highly consistent with a priory known biological information and separated from the rest of response patterns. Public Library of Science 2013-10-17 /pmc/articles/PMC3798402/ /pubmed/24147039 http://dx.doi.org/10.1371/journal.pone.0077627 Text en © 2013 Lenz et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Lenz, Michael
Schuldt, Bernhard M.
Müller, Franz-Josef
Schuppert, Andreas
PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes
title PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes
title_full PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes
title_fullStr PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes
title_full_unstemmed PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes
title_short PhysioSpace: Relating Gene Expression Experiments from Heterogeneous Sources Using Shared Physiological Processes
title_sort physiospace: relating gene expression experiments from heterogeneous sources using shared physiological processes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3798402/
https://www.ncbi.nlm.nih.gov/pubmed/24147039
http://dx.doi.org/10.1371/journal.pone.0077627
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