Statistical Methods For RNA-seq,DNA-methylation And Cancer Genome Sequencing Data

With the advantage of the next generation sequencing technology, sequence data can be much more easily obtained than ever before, with lower cost and higher speed. This article focuses on three particular kinds of sequencing data: RNA-seq, DNA-methylation and cancer genome sequencing data, and develops several new methods to analyze these data. A brand new method, termed as deGPS (implemented in R), is proposed for RNA-seq data, which contains the normalization step and the differential expression test step for RNA-seq data. We also apply generalized linear mixed model to CpG-site-based DNA-methylation data. We further conduct simulations based on real data with implementations under both frequentist and Bayesian frameworks. Finally, to detect heterogeneity in tumor cells, we introduce a brand new model and an algorithm to detect the subclones in tumor cells in cancer genome sequence studies.