README.md

In dswatson/smoothFDR: An empirical Bayes method for exploiting spatial structure in large multiple-testing problems

FDR Smoothing for Genomic Data

Overview

False discovery rate (FDR) smoothing is an empirical Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatially localized regions of significant test statistics. It then relaxes the threshold of statistical significance within these regions and tightens it elsewhere, ensuring overall FDR control at a given level. This results in increased power and cleaner spatial separation of signals from noise. The approach requires solving a nonstandard high-dimensional optimization problem, for which an efficient augmented-Lagrangian algorithm is implemented. For details, see Tansey et al.’s (2018) paper.

Installation

To install the package, run the following in R:

# Install devtools if you have not already
install.packages('devtools')

# Then install directly from GitHub
devtools::install_github('dswatson/smoothFDR')

Example

# Load library
library(smoothFDR)

# Set seed
set.seed(123)

# Import DNA methylation data, distributed with the package
data('DNAm')

# Run FDR smoothing
res <- smoothFDR(DNAm, probe = 'cpg', parallel = FALSE)

# How many significant CpG sites at 5% FDR according to Benjamini-Hochberg?
sum(res$BH_q.value <= 0.05)

## [1] 0

# How many according to smooth FDR?
sum(res$q.value <= 0.05)

## [1] 255

dswatson/smoothFDR documentation built on March 4, 2020, 3:36 a.m.