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Type: Package
Title: Estimating Local False Discovery Rates Using the Method of Moments
Version: 1.0
Date: 2020-11-17
Author: Ali Karimnezhad
Maintainer: Ali Karimnezhad <ali.karimnezhad@gmail.com>
Description: Estimation of the local false discovery rate using the method of moments.
Depends: R(≥ 2.14.2)
Encoding: UTF-8
biocViews: Bayesian, MathematicalBiology, MultipleComparison
License: GPL-3
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-11-18 02:52:23 UTC; alikarimnezhad
Repository: CRAN
Date/Publication: 2020-11-20 09:10:08 UTC

Performs a Multiple Hypothesis Testing Using the Method of Moments

Description

Based on a given vector of chi-square test statistics, provides estimates of local false discoveries.

Usage

LFDR.MM(x)

Arguments

x

A vector of chi-square test statistics with one degree of freedom.

Details

For N given features (genes, proteins, SNPs, etc.), the function tests the null hypothesis H_{0i}, i=1,\ldots,N, indicating that there is no association between feature i and a specific disease, versus its alternative hypothesis H_{1i}. For each unassociated feature i, it is suppoed that the corresponding test stiatistic x_i follows a central chi-square distribution with one degree of freedom. For each associated feature i, it is assumed that the corresponding test stiatistic x_i follows a non-central chi-square distribution with one degree of freedom and non-centrality parameter \lambda. In this packag, association is measured by estimating the local false discovery rate (LFDR), the posterior probability that the null hypothesis H_{0i} given the test statistic x_i is true. This package returns three components as mentioned in the Value section.

Value

Outputs three elements as seen below:

pi0.hat

estimate of proportion of unassocaited features \pi_0.

ncp.hat

estimate of the non-centrality parameter \lambda of the chi-square model for associated features.

lfdr.hat

estimates of local false discovery rates.

Author(s)

Code: Ali Karimnezhad.
Documentation: Ali Karimnezhad.

References

Karimnezhad, A. (2020). A Simple Yet Efficient Parametric Method of Local False Discovery Rate Estimation Designed for Genome-Wide Association Data Analysis. Retrieved from https://arxiv.org/abs/1909.13307

Examples

# vector of test statistics for assocaited features
stat.assoc<- rchisq(n=1000,df=1, ncp = 3)

# vector of test statistics for unassocaited features
stat.unassoc<- rchisq(n=9000,df=1, ncp = 0)

# vector of test statistics
stat<- c(stat.assoc,stat.unassoc)

output <- LFDR.MM(x=stat)

# Estimated pi0
output$p0.hat

# Estimated non-centrality parameter
output$ncp.hat

# Estimated LFDRs
output$lfdr.hat

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.