calculateThreshold: Calculate PPA significance threshold leading to a desired...

In iBMQ: integrated Bayesian Modeling of eQTL data

Description

In the context of multiple testing and discoveries, a popular approach is to use a common threshold leading to a desired false discovery rate (FDR). In the Bayesian paradigm, derivation of the PPA threshold is trivial and can be calculated using a direct posterior probability calculation as described in Newton et al. (2004).

Usage

calculateThreshold(prob, threshold)

Arguments

prob	matrix or data frame that contains Posterior Probability of Association (output of eqtlMcmc function).
threshold	The desired false discovery rate.

Value

cutoff

The significance threshold value

References

Newton, MA., Noueiry, A., Sarkar, D. and Ahlquist, P. (2004): "Detecting differential gene expression with a semiparametric hierarchical mixture method."Biometrics, 5(2), 155-176

Examples

data(PPA.liver)
cutoff.liver <- calculateThreshold(PPA.liver, 0.2)

iBMQ documentation built on Nov. 8, 2020, 11:04 p.m.