README.md

In jchen1981/CAMT: Covariate Adaptive Multiple Testing

CAMT

Covariate Adaptive Multiple Testing v1.1

Overview

The CAMT package implements two covariate adaptive multiple testing procedures (FDR and FWER) described in Covariate Adaptive False Discovery Rate Control with Applications to Omics-Wide Multiple Testing and Covariate Adaptive Family-wise Error Control with Applications to Genome-wide Association Studies. CAMT allows the prior null probability and/or the alternative distribution to depend on covariates. It is robust to model mis-specification and is computationally efficient. The package also contains functions for testing the informativeness of the covariates for multiple testing, and a comprehensive simulation function, which covers a wide range of settings.

Installation

# install.packages(c("matrixStats", "ggplot2", "cowplot"))
# source("https://bioconductor.org/biocLite.R")
# biocLite("qvalue")
# install.packages("devtools")
devtools::install_github("jchen1981/CAMT")

An Example

We illustrate the usage of CAMT package using simulated data.

# Load package
library("CAMT")

# Simulate data
data <- simulate.data(feature.no = 5000, covariate.strength = "Moderate", covariate.model = "pi0",
    sig.density = "Medium", sig.strength = "L3", cor.struct = "None")

# Run CAMT  
camt.obj.fdr <- camt.fdr(pvals = data$pvals, pi0.var = data$pi0.var, f1.var = data$f1.var, 
    alg.type = "EM", control.method = "knockoff+")

# Plot results (logit(pi0) vs covariate, logit(k) vs covariate)
plot.camt.fdr(camt.obj.fdr, covariate = as.vector(rank(data$pi0.var)), covariate.name = "Covariate rank",
    log = TRUE, file.name = "CovariateModerateFDR.pdf")

camt.obj.fwer <- camt.fwer(pvals = data$pvals, pi0.var = data$pi0.var)
plot.camt.fwer(camt.obj.fwer, covariate = as.vector(rank(data$pi0.var)), covariate.name = 'Covariate rank',
    log = TRUE, file.name = 'CovariateModerateFWER.pdf')    

jchen1981/CAMT documentation built on Jan. 2, 2021, 1:44 p.m.