Identifying differentially expressed genes in unreplicated multiple-treatment microarray timecourse experiments

DOI10.1016/j.csda.2004.09.004zbMath1432.62360OpenAlexW2021877722WikidataQ62108481 ScholiaQ62108481MaRDI QIDQ959180

Dan Nettleton, Eve S. Wurtele, Rhonda Decook, Carol Foster

Publication date: 11 December 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1091&context=stat_las_preprints

zbMATH Keywords

microarraysdifferentially expressed genesunreplicated data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)

Related Items

Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments

Uses Software

• R
• NumPy
• Python

Cites Work

• Statistical significance for genomewide studies
• Unnamed Item