Gene selection and clustering for time-course and dose–response microarray experiments using order-restricted inference

• Published in: Bioinformatics (Oxford, England) Vol. 19; no. 7; pp. 834 - 841
• Main Authors: Peddada, Shyamal D., Lobenhofer, Edward K., Li, Leping, Afshari, Cynthia A., Weinberg, Clarice R., Umbach, David M.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.05.2003; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological and medical sciences; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Cell Line, Tumor; Cluster Analysis; Computer Simulation; Dose-Response Relationship, Drug; Estradiol - pharmacology; Estrogens; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Genetic; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Oligonucleotide Array Sequence Analysis - methods; Sequence Alignment - methods; Sequence Analysis, DNA - methods; Statistics as Topic; Time Factors; Transcription, Genetic - genetics; Statistical analysis; Complementary DNA; Classification; Estrogen; DNA chip; Breast; Malignant tumor; Gene expression; Bioinformatics; Algorithm
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btg093

We propose an algorithm for selecting and clustering genes according to their time-course or dose–response profiles using gene expression data. The proposed algorithm is based on the order-restricted inference methodology developed in statistics. We describe the methodology for time-course experiments although it is applicable to any ordered set of treatments. Candidate temporal profiles are defined in terms of inequalities among mean expression levels at the time points. The proposed algorithm selects genes when they meet a bootstrap-based criterion for statistical significance and assigns each selected gene to the best fitting candidate profile. We illustrate the methodology using data from a cDNA microarray experiment in which a breast cancer cell line was stimulated with estrogen for different time intervals. In this example, our method was able to identify several biologically interesting genes that previous analyses failed to reveal. Contact: peddada@embryo.niehs.nih.gov * To whom correspondence should be addressed. † Present address: Amgen Inc. Thousand Oaks, CA 91320, USA.