LFDR.MM: Performs a Multiple Hypothesis Testing Using the Method of...
In LFDR.MME: Estimating Local False Discovery Rates Using the Method of Moments

Description Usage Arguments Details Value Author(s) References Examples

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

1	LFDR.MM(x)

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

For N given features (genes, proteins, SNPs, etc.), the function tests the null hypothesis H_{0i}, i=1,…,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 λ. 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.

Outputs three elements as seen below:

`pi0.hat`	estimate of proportion of unassocaited features π_0.
`ncp.hat`	estimate of the non-centrality parameter λ of the chi-square model for associated features.
`lfdr.hat`	estimates of local false discovery rates.

Code: Ali Karimnezhad.
Documentation: Ali Karimnezhad.

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

# 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