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MEMO: multi-experiment mixture model analysis of censored data
Motivation: The statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978932/ https://www.ncbi.nlm.nih.gov/pubmed/27153627 http://dx.doi.org/10.1093/bioinformatics/btw190 |
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author | Geissen, Eva-Maria Hasenauer, Jan Heinrich, Stephanie Hauf, Silke Theis, Fabian J. Radde, Nicole E. |
author_facet | Geissen, Eva-Maria Hasenauer, Jan Heinrich, Stephanie Hauf, Silke Theis, Fabian J. Radde, Nicole E. |
author_sort | Geissen, Eva-Maria |
collection | PubMed |
description | Motivation: The statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the unraveling of sources of cell-to-cell variability. Finite mixture models have shown promise, but the available approaches are ill suited to the simultaneous consideration of data from multiple experimental conditions and to censored data. The prevalence and relevance of single-cell data and the lack of suitable computational analytics make automated methods, that are able to deal with the requirements posed by these data, necessary. Results: We present MEMO, a flexible mixture modeling framework that enables the simultaneous, automated analysis of censored and uncensored data acquired under multiple experimental conditions. MEMO is based on maximum-likelihood inference and allows for testing competing hypotheses. MEMO can be applied to a variety of different single-cell data types. We demonstrate the advantages of MEMO by analyzing right and interval censored single-cell microscopy data. Our results show that an examination of censoring and the simultaneous consideration of different experimental conditions are necessary to reveal biologically meaningful subpopulation structures. MEMO allows for a stringent analysis of single-cell data and enables researchers to avoid misinterpretation of censored data. Therefore, MEMO is a valuable asset for all fields that infer the characteristics of populations by looking at single individuals such as cell biology and medicine. Availability and Implementation: MEMO is implemented in MATLAB and freely available via github (https://github.com/MEMO-toolbox/MEMO). Contacts: eva-maria.geissen@ist.uni-stuttgart.de or nicole.radde@ist.uni-stuttgart.de Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4978932 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-49789322016-08-11 MEMO: multi-experiment mixture model analysis of censored data Geissen, Eva-Maria Hasenauer, Jan Heinrich, Stephanie Hauf, Silke Theis, Fabian J. Radde, Nicole E. Bioinformatics Originals Papers Motivation: The statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the unraveling of sources of cell-to-cell variability. Finite mixture models have shown promise, but the available approaches are ill suited to the simultaneous consideration of data from multiple experimental conditions and to censored data. The prevalence and relevance of single-cell data and the lack of suitable computational analytics make automated methods, that are able to deal with the requirements posed by these data, necessary. Results: We present MEMO, a flexible mixture modeling framework that enables the simultaneous, automated analysis of censored and uncensored data acquired under multiple experimental conditions. MEMO is based on maximum-likelihood inference and allows for testing competing hypotheses. MEMO can be applied to a variety of different single-cell data types. We demonstrate the advantages of MEMO by analyzing right and interval censored single-cell microscopy data. Our results show that an examination of censoring and the simultaneous consideration of different experimental conditions are necessary to reveal biologically meaningful subpopulation structures. MEMO allows for a stringent analysis of single-cell data and enables researchers to avoid misinterpretation of censored data. Therefore, MEMO is a valuable asset for all fields that infer the characteristics of populations by looking at single individuals such as cell biology and medicine. Availability and Implementation: MEMO is implemented in MATLAB and freely available via github (https://github.com/MEMO-toolbox/MEMO). Contacts: eva-maria.geissen@ist.uni-stuttgart.de or nicole.radde@ist.uni-stuttgart.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-08-15 2016-04-19 /pmc/articles/PMC4978932/ /pubmed/27153627 http://dx.doi.org/10.1093/bioinformatics/btw190 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Originals Papers Geissen, Eva-Maria Hasenauer, Jan Heinrich, Stephanie Hauf, Silke Theis, Fabian J. Radde, Nicole E. MEMO: multi-experiment mixture model analysis of censored data |
title | MEMO: multi-experiment mixture model analysis of censored data |
title_full | MEMO: multi-experiment mixture model analysis of censored data |
title_fullStr | MEMO: multi-experiment mixture model analysis of censored data |
title_full_unstemmed | MEMO: multi-experiment mixture model analysis of censored data |
title_short | MEMO: multi-experiment mixture model analysis of censored data |
title_sort | memo: multi-experiment mixture model analysis of censored data |
topic | Originals Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978932/ https://www.ncbi.nlm.nih.gov/pubmed/27153627 http://dx.doi.org/10.1093/bioinformatics/btw190 |
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