Expression Deconvolution: A Reinterpretation of DNA Microarray Data Reveals Dynamic Changes in Cell Populations

Cells grow in dynamically evolving populations, yet this aspect of experiments often goes unmeasured. A method is proposed for measuring the population dynamics of cells on the basis of their mRNA expression patterns. The population's expression pattern is modeled as the linear combination of m...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2003-09, Vol.100 (18), p.10370-10375
Main Authors: Lu, Peng, Nakorchevskiy, Aleksey, Marcotte, Edward M.
Format: Article
Language:English
Subjects:
Artificial cells
Biological Sciences
Cell Cycle
Cell growth
Cells
Deoxyribonucleic acid
DNA
Gene Expression Profiling
Genetics
Interphase
Mathematical expressions
Messenger RNA
Mutation
Oligonucleotide Array Sequence Analysis
Phenotypes
Population dynamics
Ribonucleic acid
RNA
Saccharomyces cerevisiae - cytology
Saccharomyces cerevisiae - genetics
Yeasts
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Summary:Cells grow in dynamically evolving populations, yet this aspect of experiments often goes unmeasured. A method is proposed for measuring the population dynamics of cells on the basis of their mRNA expression patterns. The population's expression pattern is modeled as the linear combination of mRNA expression from pure samples of cells, allowing reconstruction of the relative proportions of pure cell types in the population. Application of the method, termed expression deconvolution, to yeast grown under varying conditions reveals the population dynamics of the cells during the cell cycle, during the arrest of cells induced by DNA damage and the release of arrest in a cell cycle checkpoint mutant, during sporulation, and following environmental stress. Using expression deconvolution, cell cycle defects are detected and temporally ordered in 146 yeast deletion mutants; six of these defects are independently experimentally validated. Expression deconvolution allows a reinterpretation of the cell cycle dynamics underlying all previous microarray experiments and can be more generally applied to study most forms of cell population dynamics.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1832361100