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Biomarker discovery in heterogeneous tissue samples

  • Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data areBackground: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues. Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach. Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available. Conclusions: The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.show moreshow less

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Metadaten
Author:Dirk Repsilber, Sabine Kern, Anna Telaar, Gerhard WalzlORCiD, Gillian F. Black, Joachim SelbigGND, Shreemanta K. ParidaORCiD, Stefan H. E. KaufmannORCiD, Marc JacobsenORCiD
URN:urn:nbn:de:kobv:517-opus4-429343
DOI:https://doi.org/10.25932/publishup-42934
ISSN:1866-8372
Parent Title (German):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
Subtitle (English):taking the in-silico deconfounding approach
Series (Serial Number):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (854)
Document Type:Postprint
Language:English
Date of first Publication:2020/03/18
Year of Completion:2010
Publishing Institution:Universität Potsdam
Release Date:2020/03/18
Tag:differential gene expression; gene expression matrix; heterogeneous tissue; homogeneous cell population; microdissection; quantile normalization; selection
Issue:854
Page Number:17
Source:BMC Bioinformatics 11 (2010) 27 DOI: 10.1186/1471-2105-11-27
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Peer Review:Referiert
Publication Way:Open Access
Licence (German):License LogoCreative Commons - Namensnennung 2.0 Deutschland