Deconfounding Microarray Analysis

M. Jacobsen; D. Repsilber; A. Gutschmidt; A. Neher; K. Feldmann; H. J. Mollenkopf; S. H. E. Kaufmann; A. Ziegler

doi:10.1055/s-0038-1634118

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2006; 45(05): 557-563
DOI: 10.1055/s-0038-1634118

Original Article

Schattauer GmbH

Deconfounding Microarray Analysis

Independent Measurements of Cell Type Proportions Used in a Regression Model to Resolve Tissue Heterogeneity Bias

M. Jacobsen^*

¹Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany

,

D. Repsilber^*

²Institute of Medical Biometry and Statistics, University at Lübeck, Lübeck, Germany

³Current address: Institute for Biology and Biochemistry, University Potsdam, Potsdam-Golm, Germany

,

A. Gutschmidt

¹Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany

,

A. Neher

⁴Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany

,

K. Feldmann

⁴Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany

,

H. J. Mollenkopf

⁵Microarray Core Facilities, Max Planck Institute for Infection Biology, Berlin, Germany

,

S. H. E. Kaufmann

¹Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany

,

A. Ziegler

²Institute of Medical Biometry and Statistics, University at Lübeck, Lübeck, Germany

› Author Affiliations

Abstract

Summary

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.

Keywords

Transcriptome - tissue heterogeneity - deconfounding

Full Text

References

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