Identifying significant temporal variation in time course microarray data without replicates

Stephen C. Billups, Margaret C. Neville, Michael Rudolph, Weston Porter, Pepper Schedin

Research output: Contribution to journalArticle

11 Citations (Scopus)

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. Conclusion: 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.

Original languageEnglish (US)
Article number96
JournalBMC Bioinformatics
Volume10
DOIs
StatePublished - Mar 26 2009
Externally publishedYes

Fingerprint

Microarrays
Microarray Data
Genes
Gene
Estrous Cycle
Human Mammary Glands
Rats
Cycle
Test Statistic
Percentage
Statistics
Significance Test
Gene Expression Profile
Spline Functions
Null Distribution
Gene expression
Microarray
Splines
Transcriptome
Distinct

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Structural Biology
  • Applied Mathematics

Cite this

Identifying significant temporal variation in time course microarray data without replicates. / Billups, Stephen C.; Neville, Margaret C.; Rudolph, Michael; Porter, Weston; Schedin, Pepper.

In: BMC Bioinformatics, Vol. 10, 96, 26.03.2009.

Research output: Contribution to journalArticle

Billups, Stephen C. ; Neville, Margaret C. ; Rudolph, Michael ; Porter, Weston ; Schedin, Pepper. / Identifying significant temporal variation in time course microarray data without replicates. In: BMC Bioinformatics. 2009 ; Vol. 10.
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