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1000 Titel
  • Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach
1000 Autor/in
  1. Repsilber, Dirk |
  2. Kern, Sabine |
  3. Telaar, Anna |
  4. Walzl, Gerhard |
  5. Black, Gillian F |
  6. Selbig, Joachim |
  7. Parida, Shreemanta K |
  8. Kaufmann, Stefan HE |
  9. Jacobsen, Marc |
1000 Erscheinungsjahr 2010
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2010-01-14
1000 Erschienen in
1000 Quellenangabe
  • 11: 27
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2010
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/1471-2105-11-27 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3098067/ |
1000 Ergänzendes Material
  • https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-27#Declarations |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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 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.
1000 Fachgruppe
  1. Biologie |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/UmVwc2lsYmVyLCBEaXJr|https://frl.publisso.de/adhoc/creator/S2VybiwgU2FiaW5l|https://frl.publisso.de/adhoc/creator/VGVsYWFyLCBBbm5h|https://frl.publisso.de/adhoc/creator/V2FsemwsIEdlcmhhcmQ=|https://frl.publisso.de/adhoc/creator/QmxhY2ssIEdpbGxpYW4gRg==|https://frl.publisso.de/adhoc/creator/U2VsYmlnLCBKb2FjaGlt|https://frl.publisso.de/adhoc/creator/UGFyaWRhLCBTaHJlZW1hbnRhIEs=|https://frl.publisso.de/adhoc/creator/S2F1Zm1hbm4sIFN0ZWZhbiBIRQ==|http://orcid.org/0000-0002-4703-5652
1000 Label
1000 Förderer
  1. Grand Challenges in Global Health Initiative |
  2. Bill & Melinda Gates Foundation |
1000 Fördernummer
  1. 37772
  2. -
1000 Förderprogramm
  1. project: “Biomarkers of protective immunity against Tuberculosis in the context of HIV/AIDS in Africa”
  2. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Grand Challenges in Global Health Initiative |
    1000 Förderprogramm project: “Biomarkers of protective immunity against Tuberculosis in the context of HIV/AIDS in Africa”
    1000 Fördernummer 37772
  2. 1000 joinedFunding-child
    1000 Förderer Bill & Melinda Gates Foundation |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6406288.rdf
1000 Erstellt am 2018-01-11T18:55:38.516+0100
1000 Erstellt von 218
1000 beschreibt frl:6406288
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet Fri Jan 31 02:50:08 CET 2020
1000 Objekt bearb. Wed May 02 13:06:25 CEST 2018
1000 Vgl. frl:6406288
1000 Oai Id
  1. oai:frl.publisso.de:frl:6406288 |
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1000 Sichtbarkeit Daten public
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