Evaluating test statistics to select interesting genes in microarray experiments.
A randomization procedure to evaluate the significance level and the false-discovery rate in complex microarray experiments is proposed. A related graph can be used to compare different test statistics that can be used to analyze the same experiment. This graph is closely related to receiver operator characteristic (ROC) curves. The proposed method is applied to a subset of the data from a cell-line experiment related to Huntington's disease. A small simulation study compares the effectiveness of the proposed procedure with the significance analysis of microarrays (SAM) procedure.
Kooperberg
Charles
C
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, PO Box 19024, MP 1002, Seattle, WA 98109-1024, USA. clk@fhcrc.org
Sipione
Simonetta
S
LeBlanc
Michael
M
Strand
Andrew D
AD
Cattaneo
Elena
E
Olson
James M
JM
D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006816:Huntington Disease / Q000235:genetics; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012372:ROC Curve; D011897:Random Allocation; D012044:Regression Analysis