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Bioinformatics Advance Access originally published online on October 22, 2007
Bioinformatics 2007 23(24):3328-3334; doi:10.1093/bioinformatics/btm508
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Electronically subtracting expression patterns from a mixed cell population

Mark M. Gosink 1,*, Howard T. Petrie 2 and Nicholas F. Tsinoremas 3

1Scientific Computing, 2Cancer Biology, Scripps Florida, 5353 Parkside Dr Jupiter, FL 33458 and 3Center for Computational Science, University of Miami, Miller School of Medicine, Clinical Research Building, Suite 1188, 1120 NW 14th St., Miami, FL 33136

*To whom correspondence should be addressed.


  Abstract

Motivation: Biological samples frequently contain multiple cell-types that each can play a crucial role in the development and/or regulation of adjacent cells or tissues. The search for biomarkers, or expression patterns of, one cell-type in those samples can be a complex and time-consuming process. Ordinarily, extensive laboratory bench work must be performed to separate the mixed cell population into its subcomponents, such that each can be accurately characterized.

Results: We have developed a methodology to electronically subtract gene expression in one or more components of a mixed cell population from a mixture, to reveal the expression patterns of other minor or difficult to isolate components. Examination of simulated data indicates that this procedure can reliably determine the expression patterns in cell-types that contribute as little as 5% of the total expression in a mixed cell population. We re-analyzed microarray expression data from the viral infection of macrophages and from the T-cells of wild type and Foxp3 deletion mice. Using our subtraction methodology, we were able to substantially improve the identification of genes involved in processes of subcomponent portions of these samples.

Contact: gosink{at}scripps.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Trey Ideker

Received on July 20, 2007; revised on September 19, 2007; accepted on October 4, 2007

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