Deconfounding microarray analysis - independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias.

@article{Jacobsen2006DeconfoundingMA,
  title={Deconfounding microarray analysis - independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias.},
  author={Marc Jacobsen and Dirk Repsilber and Andrea Gutschmidt and Albert Neher and Knut Feldmann and Hans Joachim Mollenkopf and Stefan H. E. Kaufmann and Andreas Ziegler},
  journal={Methods of information in medicine},
  year={2006},
  volume={45 5},
  pages={557-63}
}
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… CONTINUE READING