The Improvement In The Method Of Multi-platform Microarray Data Integration

The gene chip technology has produced large amounts of gene expression data inhigh throughput gene expression analysis. The problem of making microarray datagradually converts to checking biology hypothesis information database from a producing hypothesis tool for further analyzing, has become a research hotspot incurrent bioinformatics. One direction of research in microarray data analysis is tointegrate microarray data from different platforms after denoising and normalization,in order to increase the sample size.This thesis investigates the integration method for multi-platform microarraydata with high dimensionality and small sample size, which generate by s eparatemicroarrays but have the same objective. As a result, objectivity and stability ofintegrated data for subsequent analysis can improved.Firstly, a multiple-based integration approach is proposed. On the foundation ofthe various research, we focus on addressing the drawback of existing research,namely the data transformation will change the relative relation between genes ofspecimens, which lead to breaking the raw traits. In particular, the mean value isregarded as the an indicator for data i ntegration after the medians for all samples arecalculated. The evaluation shows that the innovative approach has higher accuracyand faster.Further, by analyzing the essence of multiple-based method, it has normalizedfeatures in the data integration process. It combination with the median rank scoresmethod which need for data normalization preprocessing, designed a compoundstructure namely two-stage data integration method. The two-stage data integrationmethod has a combination of both the advantages of multiple-based method andmedian rank scores method, not only keep the original relationship between genes,and microarray expression data from different microarrays can be compressed intothe same range, so as to ease the deviation caused by differe nt platforms experimentalenvironment. The two-stage integration method can obtain comparable or higheraccuracy and improved the stability of results.