A systematic statistical process for microarray data analysis: Countering the limitations in the public data sets

A systematic statistical process is proposed for cDNA microarray data analysis to counter the potential problems of limited replication and unknown distribution found in some public data sets. The proposed process integrates several existing methods to infer expression patterns of unknown genes. It consists of data normalization, identification of significant genes using a novel within-slide replication method coupled with conditional cluster analysis. This process uses intensity-dependent normalization to reduce dye effect. It subsequently uses one-way ANOVA with a very small cutoff p-value to identify individual genes presenting significant expression patterns across the experiments. The proposed process eventually utilizes the identified significant genes to infer expression patterns of some other genes clustered in the same groups by using k-medoids