qvalue: Estimate the q-values for a given set of p-values
In uc-bd2k/CLEAN: Clustering Enrichment Analysis

qvalue

R Documentation

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. This is a slightly modified version of the qvalue function in the qvalue package.

Usage

qvalue(p, lambda = seq(0, 0.9, 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`	he 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)

~Describe the value returned If it is a LIST, use

`call`	an estimate of the proportion of null p-values
`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 is a slightly modified version of the 'qvalue' function in the 'qvalue' package.

Author(s)

John D. Storey jstorey@u.washington.edu

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.

QVALUE Manual <URL:http://faculty.washington.edu/~jstorey/qvalue/manual.pdf>

Examples

     ## Not run:
	 p <- scan("pvalues.txt")
	 qobj <- qvalue(p)
	 qplot(qobj)
	 qwrite(qobj, filename="myresults.txt")
	 qobj <- qvalue(p, lambda=0.5, robust=TRUE)
	 qobj <- qvalue(p, fdr.level=0.05, pi0.method="bootstrap")
	 ## End(Not run)

uc-bd2k/CLEAN documentation built on Sept. 22, 2022, 4:12 a.m.