Statistical Quality Control of Microarray Gene Expression Data

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.