Description Usage Arguments Details Value Note Author(s) References See Also Examples
This function is a wrapper of subt
, extrp.pi0
and fdr
, and is
a ready to use directly on a matrix of microarray data.
1 2 3 4 5 |
dat |
a numeric matrix, which is the microarray data. Each row represent a gene, and each column
represent a subject. The first |
n1 |
a positive integer, the sample size in treatment group 1. |
n2 |
a positive integer, the sample size in treatment group 2. |
f1method |
character, the name of the function used to estimate the p-value density at one. See |
max.reps |
a positive integer, the maximum number of subsamples "per subsample size configuration". See |
balanced |
logical, indicating if only balanced subsamples are generated. See |
nparm |
either 2 or 4, indicating the number of parameters used in extrapolation. See |
extrpFUN |
character, specifying the name of the optimization function for nonlinear regression. See the |
starts |
a numeric vector of length |
plotit |
logical, indicating if the extrapolation plot will be produced. See |
This function calls subt
, extrp.pi0
, matrix.t.test
and fdr
sequentially to estimate the proportion of true null hypotheses pi0 as well as the false discovery
rates (FDR) based on the estimated pi0.
an object of class subex
, which is a list 4 components:
pi0 |
a numeric value, giving the estimated pi0 |
.
extrp.fit |
an object of class |
pvalues |
a numeric vector of length the same as |
qvalues |
a numeric vector of length the same as |
Plotting using package rgl will be tried. If not available, a warning will be generated. See plot.extrpi0
for details.
Long Qu
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 Unpublished manuscript.
subt
, extrp.pi0
, matrix.t.test
,fdr
,
plot.subex
,print.subex
1 2 3 4 5 6 7 8 9 10 11 | ## 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)
|
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