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

Description Details Author(s) References See Also Examples

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.

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

subtSubsampling a microarray data set, do t-test for each gene, and estimate p-value density at 1 for each subsample.
extrp.pi0Extrapolate the p-value density at 1 over subsample sizes to estimate the proportion of true null hypotheses.
fdrEstimate false discovery rate based on p-values and a given estimate of the proportion of true null hypotheses.
subexA wrapper that automates subt,extrp.pi0,and fdr.
combn2R Generating a sample of combinations by choosing m1 out of n1 and m2 out of n2 simultaneously.
matrix.t.testApply a t-test to each row or column of a matrix.
lastbinEstimate p-value density at 1 based on a histogram.
parncptParametrically estimate the distribution of noncentrality parameters.
nparncptNonparametrically estimate the distribution of noncentrality parameters.
sparncptSemiparametrically estimate the distribution of noncentrality parameters.
nparncppNonparametric estimate of the distribution of absolute noncentrality parameters from a large number of p-values.
CBUM(Censored) Beta-Uniform mixture model for p-values.
znormixNormal mixture model for z-scores.

Long Qu

Maintainer: Long Qu long.qu@wright.edu

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.

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

## 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));