WGCNA: qvalue – R documentation

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qvalue

Estimate the q-values for a given set of p-values

Description

Estimate the q-values for a given set of p-values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant.

Usage

qvalue(p, lambda=seq(0,0.90,0.05), pi0.method="smoother", fdr.level=NULL, robust=FALSE,
  smooth.df=3, smooth.log.pi0=FALSE)

Arguments

`p`	A vector of p-values (only necessary input)
`lambda`	The value of the tuning parameter to estimate pi_0. Must be in [0,1). Optional, see Storey (2002).
`pi0.method`	Either "smoother" or "bootstrap"; the method for automatically choosing tuning parameter in the estimation of pi_0, the proportion of true null hypotheses
`fdr.level`	A level at which to control the FDR. Must be in (0,1]. Optional; if this is selected, a vector of TRUE and FALSE is returned that specifies whether each q-value is less than fdr.level or not.
`robust`	An indicator of whether it is desired to make the estimate more robust for small p-values and a direct finite sample estimate of pFDR. Optional.
`smooth.df`	Number of degrees-of-freedom to use when estimating pi_0 with a smoother. Optional.
`smooth.log.pi0`	If TRUE and `pi0.method` = "smoother", pi_0 will be estimated by applying a smoother to a scatterplot of log pi_0 estimates against the tuning parameter lambda. Optional.

Details

If no options are selected, then the method used to estimate pi_0 is the smoother method described in Storey and Tibshirani (2003). The bootstrap method is described in Storey, Taylor & Siegmund (2004).

Value

A list containing:

`call`	function call
`pi0`	an estimate of the proportion of null p-values
`qvalues`	a vector of the estimated q-values (the main quantity of interest)
`pvalues`	a vector of the original p-values
`significant`	if fdr.level is specified, and indicator of whether the q-value fell below fdr.level (taking all such q-values to be significant controls FDR at level fdr.level)

Note

This function is adapted from package qvalue. The reason we provide our own copy is that package qvalue contains additional functionality that relies on Tcl/Tk which has led to multiple problems. Our copy does not require Tcl/Tk.

Author(s)

John D. Storey jstorey@u.washington.edu, adapted for WGCNA by Peter Langfelder

References

Storey JD. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B, 64: 479-498.

Storey JD and Tibshirani R. (2003) Statistical significance for genome-wide experiments. Proceedings of the National Academy of Sciences, 100: 9440-9445.

Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31: 2013-2035.

Storey JD, Taylor JE, and Siegmund D. (2004) Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: A unified approach. Journal of the Royal Statistical Society, Series B, 66: 187-205.

WGCNA

Weighted Correlation Network Analysis

v1.70-3

GPL (>= 2)

Authors

Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang

Initial release

2021-02-17