pi0-package: Estimating the proportion of true null hypotheses and False...

Description Details Author(s) References See Also Examples

Description

This package implements method(s) to (approximately unbiasedly) estimate the proportion of true null hypotheses, i.e., the pi0, when a very large number of hypotheses are simultaneously tested, especially for the purpose of (local) false discovery rate control for microarray data. It also contains functions to estimate the distribution of noncentrality parameters from a large number of parametric tests.

Details

Package: pi0
Type: Package
Version: 1.3-354
Date: 2014-08-22
License: GPL version 2 or newer

Author(s)

Long Qu

Maintainer: Long Qu [email protected]

References

Qu L, Nettleton D, Dekkers JCM. (2012) Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of $t$-statistics, with Applications to False Discovery Rate Estimation in Microarray Data Analysis. Biometrics, 68, 1178–1187.

Ruppert D, Nettleton D, Hwang JT. (2007) Exploring the Information in $p$-values for the Analysis and Planning of Multiple-test Experiments. Biometrics. 63. 483-495.

G.J. McLachlan, R.W. Bean and L. Ben-Tovim Jones. (2006) A Simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics, 22(13):1608-1615.

Anastasios Markitsis and Yinglei Lai (2010) A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes. Bioinformatics 26(5):640-646.

Qu, L., Nettleton, D., Dekkers, J.C.M. Subsampling Based Bias Reduction in Estimating the Proportion of Differentially Expressed Genes from Microarray Data. Unpublished manuscript.

See Also

subex, subt, extrp.pi0, fdr, combn2R, nparncpt, parncpt, sparncpt, nparncpp

Examples

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## Not run: 
set.seed(9992722)
## this is how the 'simulatedDat' data set in this package generated
simulatedDat=sim.dat(G=5000)
## this is how the 'simulatedSubex' object in this package generated
simulatedSubex=subex(simulatedDat,balanced=FALSE,max.reps=Inf,plotit=FALSE)
plot(simulatedSubex)

## End(Not run)
data(simulatedSubex); print(simulatedSubex)
## parametric, nonparametric, semiparametric estimate of 
## noncentrality parameter distribution from t-statistics
data(simulatedTstat)
simulatedTstat=head(simulatedTstat, 2000)
(npfit=nparncpt(tstat=simulatedTstat, df=8, plotit=FALSE)); 
(pfit=parncpt(tstat=simulatedTstat, df=8, zeromean=FALSE)); 
(pfit0=parncpt(tstat=simulatedTstat, df=8, zeromean=TRUE)); 
(spfit=sparncpt(npfit,pfit));

pi0 documentation built on May 2, 2019, 4:47 p.m.