Liang Chen, of the Department of Biological Sciences at the University of Southern California, will present:
Abstract: The rapid advances in high-throughput sequencing technologies provide us an opportunity to dissect transcriptomes with unprecedented resolution. However transcriptome quantification is still hindered by non-uninform read sampling. Existing methods assume a constant bias factor for each relative position of genes or simply correct the sequence-specific bias caused by random hexamer priming. Nevertheless, the overall bias is complicated and caused by multiple factors including many unknown ones. The bias pattern can vary significantly across different gene regions and different protocols. In light of these facts, we have developed a series of models to tackle the overdispersion issue in RNA-seq and single-cell RNA-seq. We proposed to use the generalized-Poisson model to estimate the bias in a data-adaptive way without any presumption. We further incorporated this data-adaptive bias correction in the deconvolution of isoform expression as well as the analysis of single-cell RNA-seq. For a more complicated scenario involving high degree of individual heterogeneity such as the analysis of the Cancer Genome Atlas (TCGA) data, we proposed a special non-parametric empirical likelihood-ratio test. Our methods significantly improve the quantification of gene expression, isoform expression, alternative exon inclusion rates, and the identification of differentially expressed genes.
A social tea will be held at 3:00 p.m. in A434 Mayo. All are Welcome.