TY - JOUR
T1 - Electronically subtracting expression patterns from a mixed cell population
AU - Gosink, Mark M.
AU - Petrie, Howard T.
AU - Tsinoremas, Nicholas F.
N1 - Funding Information:
The authors would like to thank Dr Sandra Cervino for advice on statistical analysis. They also wish to thank NCBI and the authors of the viral and regulatory T-cell datasets for making this information publicly available. M.M.G. and N.F.T. are supported by the Florida Funding Corporation. H.T.P. is supported by PHS grants AI/AG33940, AI67453 and AI/HL64665.
PY - 2007/12
Y1 - 2007/12
N2 - 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.
AB - 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.
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U2 - 10.1093/bioinformatics/btm508
DO - 10.1093/bioinformatics/btm508
M3 - Article
C2 - 17956877
AN - SCOPUS:36948998970
VL - 23
SP - 3328
EP - 3334
JO - Bioinformatics (Oxford, England)
JF - Bioinformatics (Oxford, England)
SN - 1367-4803
IS - 24
ER -