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Probabilistic analysis of gene expression measurements from heterogeneous tissues
Motivation: Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments,...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2951082/ https://www.ncbi.nlm.nih.gov/pubmed/20631160 http://dx.doi.org/10.1093/bioinformatics/btq406 |
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author | Erkkilä, Timo Lehmusvaara, Saara Ruusuvuori, Pekka Visakorpi, Tapio Shmulevich, Ilya Lähdesmäki, Harri |
author_facet | Erkkilä, Timo Lehmusvaara, Saara Ruusuvuori, Pekka Visakorpi, Tapio Shmulevich, Ilya Lähdesmäki, Harri |
author_sort | Erkkilä, Timo |
collection | PubMed |
description | Motivation: Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advantages, such as time consumption, measuring responses of multiple cell types simultaneously, keeping samples intact of external perturbations and unaltered yield of molecular content. Results: We formalize a probabilistic model, DSection, and show with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of (i) estimating cell-type proportions of heterogeneous tissue samples, (ii) estimating replication variance and (iii) identifying differential expression across cell types under various experimental conditions. As our reference we use the corresponding linear regression model, which mirrors the performance of the majority of current non-probabilistic modeling approaches. Availability and Software: All codes are written in Matlab, and are freely available upon request as well as at the project web page http://www.cs.tut.fi/∼erkkila2/. Furthermore, a web-application for DSection exists at http://informatics.systemsbiology.net/DSection. Contact: timo.p.erkkila@tut.fi; harri.lahdesmaki@tut.fi |
format | Text |
id | pubmed-2951082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-29510822010-10-08 Probabilistic analysis of gene expression measurements from heterogeneous tissues Erkkilä, Timo Lehmusvaara, Saara Ruusuvuori, Pekka Visakorpi, Tapio Shmulevich, Ilya Lähdesmäki, Harri Bioinformatics Original Paper Motivation: Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advantages, such as time consumption, measuring responses of multiple cell types simultaneously, keeping samples intact of external perturbations and unaltered yield of molecular content. Results: We formalize a probabilistic model, DSection, and show with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of (i) estimating cell-type proportions of heterogeneous tissue samples, (ii) estimating replication variance and (iii) identifying differential expression across cell types under various experimental conditions. As our reference we use the corresponding linear regression model, which mirrors the performance of the majority of current non-probabilistic modeling approaches. Availability and Software: All codes are written in Matlab, and are freely available upon request as well as at the project web page http://www.cs.tut.fi/∼erkkila2/. Furthermore, a web-application for DSection exists at http://informatics.systemsbiology.net/DSection. Contact: timo.p.erkkila@tut.fi; harri.lahdesmaki@tut.fi Oxford University Press 2010-10-15 2010-07-14 /pmc/articles/PMC2951082/ /pubmed/20631160 http://dx.doi.org/10.1093/bioinformatics/btq406 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Erkkilä, Timo Lehmusvaara, Saara Ruusuvuori, Pekka Visakorpi, Tapio Shmulevich, Ilya Lähdesmäki, Harri Probabilistic analysis of gene expression measurements from heterogeneous tissues |
title | Probabilistic analysis of gene expression measurements from heterogeneous tissues |
title_full | Probabilistic analysis of gene expression measurements from heterogeneous tissues |
title_fullStr | Probabilistic analysis of gene expression measurements from heterogeneous tissues |
title_full_unstemmed | Probabilistic analysis of gene expression measurements from heterogeneous tissues |
title_short | Probabilistic analysis of gene expression measurements from heterogeneous tissues |
title_sort | probabilistic analysis of gene expression measurements from heterogeneous tissues |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2951082/ https://www.ncbi.nlm.nih.gov/pubmed/20631160 http://dx.doi.org/10.1093/bioinformatics/btq406 |
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