Some statistical issues in microarray gene expression data.
In this paper we discuss some of the statistical issues that should be considered when conducting experiments involving microarray gene expression data. We discuss statistical issues related to preprocessing the data as well as the analysis of the data. Analysis of the data is discussed in three contexts: class comparison, class prediction and class discovery. We also review the methods used in two studies that are using microarray gene expression to assess the effect of exposure to radiofrequency (RF) fields on gene expression. Our intent is to provide a guide for radiation researchers when conducting studies involving microarray gene expression data.
Mayo
Matthew S
MS
Department of Preventive Medicine and Public Health, Center for Biostatistics and Advanced Informatics, Kansas Masonic Cancer Research Institute. mmayo@kumc.edu
Gajewski
Byron J
BJ
Morris
Jeffrey S
JS
D016477:Artifacts; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity