Matched Sample Based Approach for Cross-Platform Normalization on Gene Expression Data

Gene-expression data profile are widely used in all kinds of biomedical studies especially in cancer research. This dissertation work focus on solving the problem of how to combine datasets arising from different studies. Of particular interest is how to remove platform effect alone. The matched sample based cross-platform normalization method we developed are designed to tackle data merging problem in two scenarios: The first is affy-agilent cross-platform normalization which are belong to classic microarray gene expression profile. The second is the integration of microarray data with Next Generation Sequencing genome data. We use several general validation measures to assess and compare with the popular Distance-weighted discrimination method. With the public web-based tool NCI-60 CellMiner and The Cancer Genome Atlas data portal supported, our proposed method outperformed DWD in both cross-platform scenarios. It can be further assessed by the ability of exploring biological features in the studies of cancer type discrimination. We applied our method onto two classification problem: One is Breast cancer tumor/normal status classification on microarray and next generation sequencing datasets; The other is Breast cancer patients chemotherapy response classification on GPL96 and GPL570 microarray datasets. Both problems show the classification power are increased after our matched sample based cross-platform normalization method.