A0809
Title: False discovery rate for sparse count data with application to the discovery of hotspot in protein domain
Authors: Junyong Park - Seoul National University (Korea, South) [presenting]
Abstract: In cancer research at the molecular level, it is critical to understand which somatic mutations play an important role in the initiation or progression of cancer. Recently, studying cancer somatic variants at the protein domain level is an important area for uncovering functionally related somatic mutations. The main issue is to find the protein domain hotspots which have a significantly high frequency of mutations. Multiple testing procedures are commonly used to identify hotspots; however, when data is not large enough, existing methods produce unreliable results with failure in controlling a given type I error rate. Multiple testing procedures are proposed, based on local false discovery rate, for sparse count data, and those are applied in the identification of clusters of somatic mutations across entire gene families using protein domain models.
