Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference.
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.
Peddada
Shyamal D
SD
Biostatistics Branch, Laboratory of Molecular Carcinogenesis, Research Triangle Park, NC 27709, USA. peddada@embryo.niehs.nih.gov
Lobenhofer
Edward K
EK
Li
Leping
L
Afshari
Cynthia A
CA
Weinberg
Clarice R
CR
Umbach
David M
DM
D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism*; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D004958:Estradiol / Q000494:pharmacology; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods; D013223:Statistics as Topic; D013997:Time Factors; D014158:Transcription, Genetic / Q000235:genetics
D009363:Neoplasm Proteins; D004958:Estradiol