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A new method for identifying significant genes from gene expression data

Biometrics & Biostatistics International Journal
Yiwen Cao, Jiajuan Liang, Na Gao, Zengrong Sun

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Testing the significance of a medical treatment on experimental subjects is very common
in medical data analysis. Classical methods like the traditional analysis of variance usually
assume variance homogeneity across treatments or experimental groups of subjects.
However, this assumption is often violated if there exists fundamental difference between
experimental groups like male and female groups of patients. In this paper, we propose to
use a theoretically proved exact F -test for testing the significance of a medical treatment
for subjects before and after the treatment. This new exact F -test is applicable regardless
of variance homogeneity across groups. An illustration based on real experimental data
from treatments on rats shows that the new exact F -test gives more convincing results
than those from the traditional analysis of variance.


analysis of variance; F -test; gene expression data; multiple mean comparison