calculateThreshold {iBMQ} | R Documentation

## Calculate PPA significance threshold leading to a desired false discovery rate

### 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)

[Package iBMQ version 1.26.0 Index]