Correlating measurements across samples improves accuracy of large-scale expression profile experiments.
Gene expression profiling technologies suffer from poor reproducibility across replicate experiments. However, when analyzing large datasets, probe-level expression profile correlation can help identify flawed probes and lead to the construction of truer probe sets with improved reproducibility. We describe methods to eliminate uninformative and flawed probes, account for dependence between probes, and address variability due to transcript-isoform mixtures. We test and validate our approach on Affymetrix microarrays and outline their future adaptation to other technologies.
Alvarez
Mariano Javier
MJ
Joint Centers for Systems Biology, Columbia University, 2960 Broadway, New York, NY 10027-6900, USA. malvarez@c2b2.columbia.edu
Sumazin
Pavel
P
Rajbhandari
Presha
P
Califano
Andrea
A
D000465:Algorithms; D015342:DNA Probes / Q000235:genetics; D003625:Data Collection / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012984:Software
D015342:DNA Probes