报告题目：Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities
主 讲 人：郑小琪 教授
报告摘要：Inference of differentially methylated CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of con-founding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of differentially methylated sites if not properly accounted for. We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma, and limma corrected by tumor purity using simulated data sets. Our method shows significantly better performance at different levels of differential methylation threshold, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose.
个人简历：郑小琪，上海师范大学数理学院教授，博士生导师。主要从事统计学习和计算生物学领域的研究。2010年至今，已经在 Genome Biology, PLoS Computational Biology, Bioinformatics 等杂志发表论文 60 余篇，其中第一或通讯作者 37 篇，累计影响因子超过 200。主持国家自然科学基金青年项目1项，面上项目2项。