hem.fdr {HEM} | R Documentation |
Computes resampling-based False Discovery Rate (FDR)
hem.fdr(dat, n.layer, design, rep=TRUE, 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)
dat |
data |
n.layer |
number of layers: 1=one-layer EM; 2=two-layer EM |
design |
design matrix |
rep |
no replication if FALSE |
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. |
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 |
HyungJun Cho and Jae K. Lee
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)