LBE {LBE} | R Documentation |
LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate and the q-values.
LBE(pval, a = NA, l = 0.05, ci.level = 0.95, qvalues = TRUE, plot.type = "main", FDR.level = 0.05, n.significant = NA)
pval |
Numerical vector of p-values (only necessary input). |
a |
Real value used in $ [-ln (1-pi)]^{a} $ (see details). If a==NA (default), then the value of a is automatically calculated as the greatest value such that the upper bound of the asymptotic standard deviation of the estimator of pi0 is smaller than the threshold l. If a>=1, the value of a is used in $ [-ln (1-pi)]^a $ (see details). If a<1, the identity function is used for transforming the p-values. |
l |
Threshold for the upper bound of the asymptotic standard deviation (only used if a==NA). |
ci.level |
Level for the confidence interval of pi0. |
qvalues |
Logical value for estimating the qvalues and the FDR. If qvalues=FALSE, only the proportion pi0 of true null hypotheses is estimated. |
plot.type |
If plot.type="none", no graphic is displayed. If plot.type="main", the estimated q-values versus the p-values are plotted together with the histogram of the p-values. If plot.type="multiple", several graphics are displayed: 1. The histogram of the p-values 2. The estimated q-values versus the p-values 3. The number of significant tests versus each qvalue cutoff 4. The number of expected false positives versus the number of significant tests. |
FDR.level |
Level at which to control the FDR (only used if n.significant==NA). |
n.significant |
If specified, the FDR is estimated for the rejection region defined by the "n.significant" smallest p-values. |
The procedure LBE is based on the expectation of a particular transformation of the p-values leading to a straightforward estimation of the key quantity pi0 that is the proportion of true null hypotheses: $ pi0(a)={(1/m)*sum_{i=1}^{m}[-ln(1-pi)]^a]}/Gamma(a+1) $ where a belongs to the interval [1;inf[.
A list containing:
call |
Function call. |
FDR |
Level at which to control the FDR (if n.significant==NA) or estimated FDR (if n.significant!=NA). |
pi0 |
Estimated value of pi0, the proportion of true null hypotheses. |
pi0.ci |
Confidence interval for pi0. |
ci.level |
Level for the confidence interval of pi0. |
a |
Value used in $ [-ln (1-pi)]^a $ (see details). |
l |
Upper bound of the asymptotic standard deviation for pi0. |
qvalues |
Vector of the estimated q-values. |
pvalues |
Vector of the original p-values. |
significant |
Indicator of wether the null hypothesis is rejected. |
n.significant |
Number of rejected null hypotheses. |
LBE is an alternative method to the one proposed by Storey and Tibshirani (2003) for estimating the q-values, this latter method being implemented in the package {qvalue}.
Cyril Dalmasso
Dalmasso C, Broet P, Moreau T (2005). A simple procedure for estimating the false discovery rate. Bioinformatics. Bioinformatics, 21: 660 - 668.
Storey JD and Tibshirani R. (2003). Statistical significance for genome-wide studies. Proc Natl Acad Sci, 100, 9440-9445.
LBEplot
, LBEsummary
, LBEwrite
, LBEa
## start data(hedenfalk.pval) res=LBE(hedenfalk.pval) data(golub.pval) res=LBE(golub.pval) ## end