Description:

Li-Xuan Qin, PhD

Associate Member, Memorial Sloan Kettering Cancer Center, New York, NY

Presentation Title:

Depth Normalization for MicroRNA Sequencing: Method Assessment

Abstract:

A crucial step to derive high-quality transcriptomics data is to identify data artifacts caused by systematic differences in the processing of specimens and to remove these artifacts by data normalization. Statistical methods for depth normalization have been recently developed for RNA sequencing, including both simple re-scaling-based methods and regression-based methods. Many of these methods rely on the presupposition that variations in the assumed scaling factor or in the projection of the assumed regression function are solely due to data artifacts and should be removed. Their performance is yet to be understood for the sequencing of microRNAs, a unique class of small RNAs regulating gene expression and closely linked to carcinogenesis, which are low-complexity and tend to be expressed in a tissue-specific manner. We assessed the performance of existing normalization methods using robustly benchmarked and realistically distributed data. We also developed a data-driven and biology-motivated approach to more objectively guide the selection of a depth normalization method for a dataset under study, and demonstrated its use with data from the Cancer Genome Atlas.

For Zoom link please contact Lucia Ontiveros : lucia.ontiveros@northwestern.edu