A0666
Title: Double truncation method for controlling local false discovery rate in case of spiky null
Authors: Jaesik Jeong - Chonnam National University (Korea, South) [presenting]
Abstract: Many multiple test procedures, which control the false discovery rate, have been developed to identify some cases (e.g., genes) showing statistically significant differences between two different groups. However, a common issue encountered in some practical data sets is the presence of highly spiky null distributions. Existing methods struggle to control type I error in such cases due to the inflated false positives," but this problem has not been addressed in previous literature. This issue has been recently encountered while analyzing SET4 gene deletion data and proposed modeling the null distribution using a scale mixture normal distribution. However, the use of this approach is limited due to strong assumptions on the spiky peak. A novel multiple-test procedure is presented that can be applied to any type of spiky peak data, including situations with no spiky peak or with one or two spiky peaks. It is demonstrated numerically that the proposed method effectively controls the false discovery rate at the desired level using simulated data. Furthermore, the method is applied to two real data sets, namely the SET protein data and peony data.
