High-Dimensional Interaction Detection with False Sign Rate Control*

2021 ◽  
pp. 1-33
Author(s):  
Daoji Li ◽  
Yinfei Kong ◽  
Yingying Fan ◽  
Jinchi Lv
Keyword(s):  
Rate Control ◽  
High Dimensional ◽  
Interaction Detection ◽  
Dimensional Interaction

scholarly journals SNP interaction detection with Random Forests in high-dimensional genetic data

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Stacey J Winham ◽  
Colin L Colby ◽  
Robert R Freimuth ◽  
Xin Wang ◽  
Mariza de Andrade ◽  
...  
Keyword(s):  
Random Forests ◽  
Genetic Data ◽  
High Dimensional ◽  
Interaction Detection ◽  
Snp Interaction

A Diagnostic Procedure for High-Dimensional Data Streams via Missed Discovery Rate Control

Technometrics ◽  
2019 ◽  
Vol 62 (1) ◽  
pp. 84-100 ◽  
Author(s):  
Wendong Li ◽  
Dongdong Xiang ◽  
Fugee Tsung ◽  
Xiaolong Pu
Keyword(s):  
Data Streams ◽  
Rate Control ◽  
Diagnostic Procedure ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 230
Author(s):  
Fang Xie ◽  
Johannes Lederer
Keyword(s):  
Microbial Diversity ◽  
Rate Control ◽  
Gut Microbiome ◽  
High Dimensional ◽  
Health And Wellbeing ◽  
False Discovery ◽  
Recent Approach ◽  
Lasso Estimator ◽  
Microbiome Data ◽  
False Discoveries

Recent discoveries suggest that our gut microbiome plays an important role in our health and wellbeing. However, the gut microbiome data are intricate; for example, the microbial diversity in the gut makes the data high-dimensional. While there are dedicated high-dimensional methods, such as the lasso estimator, they always come with the risk of false discoveries. Knockoffs are a recent approach to control the number of false discoveries. In this paper, we show that knockoffs can be aggregated to increase power while retaining sharp control over the false discoveries. We support our method both in theory and simulations, and we show that it can lead to new discoveries on microbiome data from the American Gut Project. In particular, our results indicate that several phyla that have been overlooked so far are associated with obesity.

scholarly journals FreeHi-C: high fidelity Hi-C data simulation for benchmarking and data augmentation

2019 ◽  
Author(s):  
Ye Zheng ◽  
Sündüz Keleş
Keyword(s):  
Rate Control ◽  
Data Augmentation ◽  
Data Driven ◽  
Detection Methods ◽  
Chromatin Interaction ◽  
High Fidelity ◽  
Data Simulation ◽  
Interaction Detection ◽  
False Discovery ◽  
Wide Range

AbstractAbility to simulate realistic high-throughput chromatin conformation (Hi-C) data is foundational for developing and benchmarking statistical and computational methods for Hi-C data analysis. We propose FreeHi-C, a data-driven Hi-C simulator for simulating and augmenting Hi-C datasets. FreeHi-C employs a non-parametric strategy for estimating interaction distribution of genome fragments from a given sample and simulates Hi-C reads from interacting fragments. Data from FreeHi-C exhibit higher fidelity to the biological Hi-C data compared with other tools in its class. FreeHi-C not only enables benchmarking a wide range of Hi-C analysis methods but also boosts the precision and power of differential chromatin interaction detection methods while preserving false discovery rate control through data augmentation.

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