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Deconfounding microarray analysis: independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias
Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
Institute of Medical Biometry and Statistics, University at Lübeck, Lübeck, Germany; Institute for Biology and Biochemistry, University Potsdam, Potsdam-Golm, Germany.ORCID iD: 0000-0002-7173-5579
Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany .
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2006 (English)In: Methods of Information in Medicine, ISSN 0026-1270, Vol. 45, no 5, p. 557-63Article in journal (Refereed) Published
Abstract [en]

Objectives: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type.

Methods: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data are determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes.

Results: For our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Fitting an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results.

Conclusions: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.

Place, publisher, year, edition, pages
Stuttgart, Germany: Schattauer Gmbh, 2006. Vol. 45, no 5, p. 557-63
Keywords [en]
transcriptome, tissue heterogeneity, deconfounding
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-40716ISI: 000241149400013PubMedID: 17019511Scopus ID: 2-s2.0-33750219625OAI: oai:DiVA.org:oru-40716DiVA, id: diva2:778524
Available from: 2015-01-10 Created: 2015-01-10 Last updated: 2018-01-30Bibliographically approved

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