Abstract
BackgroundAn 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.ResultsA 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.ConclusionThe 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.
Highlights
An important component of time course microarray studies is the identification of genes that demonstrate significant time-dependent variation in their expression levels
As part of this study, we developed an algorithm for identifying genes with significant temporal variation that does not rely on replicates
Algorithm description To describe our algorithm, we assume that gene expression data were collected at T time points denoted t1,..., tT, given in nondecreasing order (i.e., t1 ≤ t2 ≤ ... ≤ tT)
Summary
An important component of time course microarray studies is the identification of genes that demonstrate significant time-dependent variation in their expression levels. Microarrays allow researchers to take a "snapshot" of the state of a cell by measuring the mRNA expression levels of thousands of genes simultaneously By taking multiple such "snapshots" at different times, one gains a dynamic picture of how expression levels change over time. There are important differences between static experiments and time course experiments, which have motivated the development of specialized methods for analyzing time course data. There may be little or no mathematical relationship between these conditions, so they are usually represented as categorical data In such experiments, it is essential to have several replicates from each condition. Time is a quantitative variable, so the order of the data and the spacing between time points matters This difference can be exploited to develop more powerful techniques for analyzing time course data. There is no longer an inherent requirement for replicates
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