title: Identifying significant temporal variation in time course microarray data without replicates.
abstract: BACKGROUND
  An important component of time course microarray studies is the identification of genes that demonstrate significant time-dependent variation in their expression levels. Until recently, available methods for performing such significance tests required replicates of individual time points. This paper describes a replicate-free method that was developed as part of a study of the estrous cycle in the rat mammary gland in which no replicate data was collected.
  
  
  RESULTS
  A temporal test statistic is proposed that is based on the degree to which data are smoothed when fit by a spline function. An algorithm is presented that uses this test statistic together with a false discovery rate method to identify genes whose expression profiles exhibit significant temporal variation. The algorithm is tested on simulated data, and is compared with another recently published replicate-free method. The simulated data consists both of genes with known temporal dependencies, and genes from a null distribution. The proposed algorithm identifies a larger percentage of the time-dependent genes for a given false discovery rate. Use of the algorithm in a study of the estrous cycle in the rat mammary gland resulted in the identification of genes exhibiting distinct circadian variation. These results were confirmed in follow-up laboratory experiments.
  
  
  CONCLUSIONS
  The proposed algorithm provides a new approach for identifying expression profiles with significant temporal variation without relying on replicates. When compared with a recently published algorithm on simulated data, the proposed algorithm appears to identify a larger percentage of time-dependent genes for a given false discovery rate. The development of the algorithm was instrumental in revealing the presence of circadian variation in the virgin rat mammary gland during the estrous cycle.
authors.affiliation: Department of Mathematical and Statistical Sciences, University of Colorado, Denver, CO, USA. Stephen.Billups@ucdenver.edu
authors.forename: Stephen C
authors.initials: SC
authors.lastname: Billups
authors.forename: Margaret C
authors.initials: MC
authors.lastname: Neville
authors.forename: Michael
authors.initials: M
authors.lastname: Rudolph
authors.forename: Weston
authors.initials: W
authors.lastname: Porter
authors.forename: Pepper
authors.initials: P
authors.lastname: Schedin