|
Activity Number:
|
21
- Aligning Data Normalization with Analysis Goals for Reproducible Research
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Biometrics Section
|
|
Abstract #303012
|
|
|
Title:
|
Multiple Testing Under Dependence and Non-Sparsity with Applications in Genomics and Toxicology
|
|
Author(s):
|
Hongyuan Cao* and Shyamal Peddada and Li-Xuan Qin
|
|
Companies:
|
Florida State University and University of Pittsburgh and Memorial Sloan Kettering Cancer Center
|
|
Keywords:
|
|
|
Abstract:
|
High throughput technologies enable simultaneous inference of complex high dimensional data. An acute problem is the multiple testing adjustment. Most existing literature examine the problem under independence and sparsity assumptions. We propose a multiple testing procedure to incorporate dependence and non-sparsity features inherent in many high dimensional data, such as microRNA in genomics and quantitative high throughput screening (qHTS) assays in toxicology.
|
Authors who are presenting talks have a * after their name.
