title: Polynomial model approach for resynchronization analysis of cell-cycle gene expression data.
abstract: BACKGROUND
  Identification of genes expressed in a cell-cycle-specific periodical manner is of great interest to understand cyclic systems which play a critical role in many biological processes. However, identification of cell-cycle regulated genes by raw microarray gene expression data directly is complicated by the factor of synchronization loss, thus remains a challenging problem. Decomposing the expression measurements and extracting synchronized expression will allow to better represent the single-cell behavior and improve the accuracy in identifying periodically expressed genes.
  
  
  RESULTS
  In this paper, we propose a resynchronization-based algorithm for identifying cell-cycle-related genes. We introduce a synchronization loss model by modeling the gene expression measurements as a superposition of different cell populations growing at different rates. The underlying expression profile is then reconstructed through resynchronization and is further fitted to the measurements in order to identify periodically expressed genes. Results from both simulations and real microarray data show that the proposed scheme is promising for identifying cyclic genes and revealing underlying gene expression profiles.
  
  
  BACKGROUND
  Contact the authors.
  
  
  BACKGROUND
  Supplementary data are available at: http://dsplab.eng.umd.edu/~genomics/syn/
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, USA. qiupeng@umd.edu
authors.forename: Peng
authors.initials: P
authors.lastname: Qiu
authors.forename: Z Jane
authors.initials: ZJ
authors.lastname: Wang
authors.forename: K J Ray
authors.initials: KJ
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D018797:Cell Cycle Proteins / Q000378:metabolism*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D009716:Numerical Analysis, Computer-Assisted; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010507:Periodicity; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*
chemical_list: D018797:Cell Cycle Proteins; D029701:Saccharomyces cerevisiae Proteins