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Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89
Computational expression deconvolution aims to estimate the contribution of individual cell populations to expression profiles measured in samples of heterogeneous composition. Zhong et al. recently proposed Digital Sorting Algorithm (BMC Bioinformatics 2013 Mar 7;14:89) and showed that they could a...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4245730/ https://www.ncbi.nlm.nih.gov/pubmed/25431099 http://dx.doi.org/10.1186/s12859-014-0347-5 |
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author | Kuhn, Alexandre |
author_facet | Kuhn, Alexandre |
author_sort | Kuhn, Alexandre |
collection | PubMed |
description | Computational expression deconvolution aims to estimate the contribution of individual cell populations to expression profiles measured in samples of heterogeneous composition. Zhong et al. recently proposed Digital Sorting Algorithm (BMC Bioinformatics 2013 Mar 7;14:89) and showed that they could accurately estimate population-specific expression levels and expression differences between two populations. They compared DSA with Population-Specific Expression Analysis (PSEA), a previous deconvolution method that we developed to detect expression changes occurring within the same population between two conditions (e.g. disease versus non-disease). However, Zhong et al. compared PSEA-derived specific expression levels across different cell populations. Specific expression levels obtained with PSEA cannot be directly compared across different populations as they are on a relative scale. They are accurate as we demonstrate by deconvolving the same dataset used by Zhong et al. and, importantly, allow for comparison of population-specific expression across conditions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0347-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4245730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42457302014-11-28 Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89 Kuhn, Alexandre BMC Bioinformatics Correspondence Computational expression deconvolution aims to estimate the contribution of individual cell populations to expression profiles measured in samples of heterogeneous composition. Zhong et al. recently proposed Digital Sorting Algorithm (BMC Bioinformatics 2013 Mar 7;14:89) and showed that they could accurately estimate population-specific expression levels and expression differences between two populations. They compared DSA with Population-Specific Expression Analysis (PSEA), a previous deconvolution method that we developed to detect expression changes occurring within the same population between two conditions (e.g. disease versus non-disease). However, Zhong et al. compared PSEA-derived specific expression levels across different cell populations. Specific expression levels obtained with PSEA cannot be directly compared across different populations as they are on a relative scale. They are accurate as we demonstrate by deconvolving the same dataset used by Zhong et al. and, importantly, allow for comparison of population-specific expression across conditions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0347-5) contains supplementary material, which is available to authorized users. BioMed Central 2014-11-28 /pmc/articles/PMC4245730/ /pubmed/25431099 http://dx.doi.org/10.1186/s12859-014-0347-5 Text en © Kuhn; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Correspondence Kuhn, Alexandre Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89 |
title | Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89 |
title_full | Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89 |
title_fullStr | Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89 |
title_full_unstemmed | Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89 |
title_short | Correspondence regarding Zhong et al., BMC Bioinformatics 2013 Mar 7;14:89 |
title_sort | correspondence regarding zhong et al., bmc bioinformatics 2013 mar 7;14:89 |
topic | Correspondence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4245730/ https://www.ncbi.nlm.nih.gov/pubmed/25431099 http://dx.doi.org/10.1186/s12859-014-0347-5 |
work_keys_str_mv | AT kuhnalexandre correspondenceregardingzhongetalbmcbioinformatics2013mar71489 |