A geometric approach to determine association and coherence of the activation times of cell-cycling genes under differing experimental conditions.
Differing arresting agents and protocols can be used to synchronize cells in cultures to specific phases of the cell when studying cell-cycle gene expressions. Often, data derived from individual experiments are analyzed separately, since no appropriate statistical methodology is available at the moment to analyze the data from all such experiments simultaneously. The focus of this paper is to determine the association and coherence of the relative activation times of cell-cycling genes under different experimental conditions. Using a circular-circular regression model, we define two parameters, a rotation parameter for the angular difference between cells' arresting times (phases) in two cell-cycle experiments, and an association parameter to describe the correspondence between the cycle times of maximal expression (phase angles) for a set of genes studied in two experiments. Further, we propose a procedure to assess coherence across multiple experiments, i.e. to what extent the circular ordering of the phase angles of genes is maintained across multiple experiments. Coherence of genes across experiments suggests that functionally these genes tend to respond in a stereotypically sequenced way under different experimental conditions. Our proposed methodology is illustrated by applying it to a HeLa cell-cycle gene-expression data.
Liu
Delong
D
Biostatistics Branch, MD:A3-03, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, USA.
Weinberg
Clarice R
CR
Peddada
Shyamal D
SD
D000465:Algorithms; D002453:Cell Cycle / Q000502:physiology*; D018797:Cell Cycle Proteins / Q000235:genetics* / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D018816:Genes, cdc / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D013223:Statistics as Topic; D013997:Time Factors
D018797:Cell Cycle Proteins