TY - JOUR
T1 - Deconfounding microarray analysis: Independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias
AU - Jacobsen, M.
AU - Repsilber, D.
AU - Gutschmidt, A.
AU - Neher, A.
AU - Feldmann, K.
AU - Mollenkopf, H. J.
AU - Kaufmann, S. H.E.
AU - Ziegler, Andreas
PY - 2006
Y1 - 2006
N2 - Objectives: Microorray 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 micraarray 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 monocyre 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 interptetability, and hence the validity of transcriptome profiling results.
AB - Objectives: Microorray 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 micraarray 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 monocyre 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 interptetability, and hence the validity of transcriptome profiling results.
UR - http://www.scopus.com/inward/record.url?scp=33750219625&partnerID=8YFLogxK
U2 - 10.1055/s-0038-1634118
DO - 10.1055/s-0038-1634118
M3 - Journal articles
C2 - 17019511
AN - SCOPUS:33750219625
SN - 0026-1270
VL - 45
SP - 557
EP - 563
JO - Methods of Information in Medicine
JF - Methods of Information in Medicine
IS - 5
ER -