hem.fdr {HEM}R Documentation

FDR Evaluation

Description

Computes resampling-based False Discovery Rate (FDR)

Usage

hem.fdr(dat,  tr=" ", n.layer, design, hem.out, eb.out=NULL, n.iter=5, q.trim=0.9, 
        target.fdr=c(0.001,0.005,0.01,0.05,0.1,0.15,0.20,0.30,0.40,0.50), 
        n.digits=10, print.message.on.screen=TRUE)

Arguments

dat data
tr if "log2", "log10", or "loge", then log-transformation (with base 2, 10, or e respectively) is taken.
n.layer number of layers: 1=one-layer EM; 2=two-layer EM
design design matrix
hem.out output from hem function
eb.out output from hem.eb.prior function
n.iter number of iterations
q.trim quantile used for estimtaing the proportion of true negatives (pi0)
target.fdr Target FDRs
n.digits number of digits
print.message.on.screen if TRUE, process status is shown on screen.

Value

fdr H-values and corresponding FDRs
pi0 estimated proportion of true negatives
H.null H-scores from null data
targets given target FDRs, corrsponding critical values and numbers of significant genes are provided

Author(s)

HyungJun Cho and Jae K. Lee

See Also

hem.eb.prior hem

Examples


data(pbrain)

##construct a design matrix
cond <- c(1,1,1,1,1,1,2,2,2,2,2,2)
ind  <- c(1,1,2,2,3,3,1,1,2,2,3,3)
rep  <- c(1,2,1,2,1,2,1,2,1,2,1,2)
design <- data.frame(cond,ind,rep)

##normalization
pbrain.nor <- hem.preproc(pbrain[,2:13])

##take a subset for a testing purpose;
##use all genes for a practical purpose
pbrain.nor <- pbrain.nor[1:1000,]

##estimate hyperparameters of variances by LPE
pbrain.eb  <- hem.eb.prior(pbrain.nor, n.layer=2,  design=design,
                           method.var.e="neb", method.var.b="peb")     

##fit HEM with two layers of error
##using the small numbers of burn-ins and MCMC samples for a testing purpose;
##but increase the numbers for a practical purpose 
pbrain.hem <- hem(pbrain.nor, n.layer=2,  design=design,burn.ins=10, n.samples=30, 
              method.var.e="neb", method.var.b="peb", 
              var.e=pbrain.eb$var.e, var.b=pbrain.eb$var.b)

##Estimate FDR based on resampling 
pbrain.fdr <- hem.fdr(pbrain.nor, n.layer=2,  design=design, 
                  hem.out=pbrain.hem, eb.out=pbrain.eb)


[Package HEM version 1.0.4 Index]