Simultaneous inference for RNA-Seq data
Year: 2012
Type: dissertation
Abstract: In the last few years, RNA-Seq has become a popular choice for high-throughput studies of gene expression, revealing its potential to overcome microarrays and become the new standard for transcriptional profiling. At a gene-level, RNA-Seq yields counts rather than continuous measures of expression, leading to the need for novel methods to deal with count data in high-dimensional problems. In this Thesis, we aim at shedding light on the problems related to the exploration and modeling of RNA-Seq data. In particular, we introduce simple and effective ways to summarize and visualize the data; we define a novel algorithm for the clustering of RNA-Seq data and we implement simple normalization strategies to deal with technology-related biases. Finally, we present a hierarchical Bayesian approach to the modeling of RNA-Seq data. The model accounts for the difference in sequencing depth, as well as for overdispersion, automatically accounting for different types of normalization.
Author: Davide Risso
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2276656318