Statistical Quality Control of Microarray Gene Expression Data
Shen Lu, Richard S. Segall
This paper is about how to control the quality of
microarray expression data. Since gene-expression
microarrays have become almost as widely used as
measurement tools in biological research, we survey
microarray experimental data to see possibilities and
problems to control microarray expression data. We use
both variable measure and attribute measure to visualize
microarray expression data. According to the attribute
data's structure, we use control charts to visualize fold
change and t-test attributes in order to find the root causes.
Then, we build data mining prediction models to evaluate
the output. According to the accuracy of the prediction
model, we can prove control charts can effectively
visualize root causes.