Similarities of ordered gene lists.
BACKGROUND
Many applications of microarray technology in clinical cancer studies aim at detecting molecular features for refined diagnosis. In this paper, we follow an opposite rationale: we try to identify common molecular features shared by phenotypically distinct types of cancer using a meta-analysis of several microarray studies. We present a novel algorithm to uncover that two lists of differentially expressed genes are similar, even if these similarities are not apparent to the eye. The method is based on the ordering in the lists.


RESULTS
In a meta-analysis of five clinical microarray studies we were able to detect significant similarities in five of the ten possible comparisons of ordered gene lists. We included studies, where not a single gene can be significantly associated to outcome. The detection of significant similarities of gene lists from different microarray studies is a novel and promising approach. It has the potential to improve upon specialized cancer studies by exploring the power of several studies in one single analysis. Our method is complementary to previous methods in that it does not rely on strong effects of differential gene expression in a single study but on consistent ones across multiple studies.
Yang
Xinan
X
Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestr. 73, 14195 Berlin, Germany. xinan.yang@molgen.mpg.de
Bentink
Stefan
S
Scheid
Stefanie
S
Spang
Rainer
R
D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D030541:Databases, Genetic; D015536:Down-Regulation; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D005910:Glioma / Q000235:genetics; D006801:Humans; D008297:Male; D008654:Mesothelioma / Q000235:genetics; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000235:genetics; D012984:Software; D015854:Up-Regulation