Comparing microarray studies.
We present a practical guide to some of the issues involved in comparing or integrating different microarray studies. We discuss the influence that various factors have on the agreement between studies, such as different technologies and platforms, statistical analysis criteria, protocols, and lab variability. We discuss methods to carry out or refine such comparisons, and detail several common pitfalls to avoid. Finally, we illustrate these ideas with an example case.
Suárez-Fariñas
Mayte
M
Center for Studies in Physics and Biology, The Rockefeller University, New York, NY, USA.
Magnasco
Marcelo O
MO
D000328:Adult; D019070:Cell Lineage; D018076:DNA, Complementary; D003627:Data Interpretation, Statistical; D053595:Embryonic Stem Cells / Q000166:cytology / Q000378:metabolism; D006801:Humans; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D008967:Molecular Biology / Q000379:methods*; D009693:Nucleic Acid Hybridization; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation / Q000379:methods*; D011786:Quality Control; D012333:RNA, Messenger / Q000378:metabolism; D015203:Reproducibility of Results
D018076:DNA, Complementary; D012333:RNA, Messenger