Speaker: Jaesik Jeong (Chonnam National University)

Title: Double truncation method for controlling local false discovery rate in case of spiky null

Abstract:

Many multiple test procedures, which control false discovery rate (FDR), have been developed to identify some cases (e.g. genes) showing statistically significant difference between groups. Highly spiky null is often reported in some data sets from practice. When it occurs, currently existing methods have a difficulty of controlling type I error due to the ‘inflated false positives’. No attention has been given to this in previous literature. Recently, a part of us has encountered the problem in the analysis of SET4 gene deletion data and proposed to model the null with a scale mixture normal distribution. However, its use is very limited due to the strong assumptions on the spiky peak (e.g. symmetric peak with respect to 0). In this paper, we propose a new multiple test procedure that can be applied to any type of spiky peak data, even to the situation with no spiky peak or with more than one spiky peaks. In our procedure, we truncate the central statistics around 0, which mainly contributes to the spike of the null, as well as two tails that are possibly contaminated by the alternative. We name it as ‘double truncation method’. After double truncation, the null density estimation is done by the doubly truncated maximum likelihood estimator (DTMLE). We numerically show that the proposed method controls the false discovery rate at the aimed level on simulated data. Also, we apply our method to two real data sets such as SET protein data and peony data.

The seminar will be held at 10AM on Tuesday 7th of February 2023, in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).

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