Error control variability in pathway-based microarray analysis.
BACKGROUND
The decision to commit some or many false positives in practice rests with the investigator. Unfortunately, not all error control procedures perform the same. Our problem is to choose an error control procedure to determine a P-value threshold for identifying differentially expressed pathways in high-throughput gene expression studies. Pathway analysis involves fewer tests than differential gene expression analysis, on the order of a few hundred. We discuss and compare methods for error control for pathway analysis with gene expression data.


RESULTS
In consideration of the variability in test results, we find that the widely used Benjamini and Hochberg's (BH) false discovery rate (FDR) analysis is less robust than alternative procedures. BH's error control requires a large number of hypothesis tests, a reasonable assumption for differential gene expression analysis, though not the case with pathway-based analysis. Therefore, we advocate through a series of simulations and applications to real gene expression data that researchers control the number of false positives rather than the FDR.
Gold
David L
DL
Department of Biostatistics, Roswell Park Cancer, Buffalo, NY, USA. dlgold@buffalo.edu
Miecznikowski
Jeffrey C
JC
Liu
Song
S
D003198:Computer Simulation; D004314:Down Syndrome / Q000235:genetics; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007947:Leukemia, Megakaryoblastic, Acute / Q000235:genetics; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012107:Research Design; D012907:Smoking / Q000235:genetics; D002583:Uterine Cervical Neoplasms / Q000235:genetics