hem.eb.prior {HEM} | R Documentation |
Empirical Bayes (EB) Prior Specification
Description
Estimates experimental and biological variances by LPE and resampling
Usage
hem.eb.prior(dat, tr=" ", n.layer, design,
method.var.e="neb", method.var.b="peb", method.var.t="neb",
q=0.01, B=25, 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 |
design |
design matrix |
method.var.e |
prior specification method for experimental variance;
"peb"=parametric EB prior specification, "neb"=nonparametric EB prior specification |
method.var.b |
prior specification method for biological variance;
"peb"=parametric EB prior specification |
method.var.t |
prior specification method for total variance;
"peb"=parametric EB prior specification, "neb"=nonparametric EB prior specification |
q |
quantile for paritioning genes based on expression levels |
B |
number of iterations for resampling |
n.digits |
number of digits |
print.message.on.screen |
if TRUE, process status is shown on screen. |
Value
var.b |
prior estimate matrix for biological variances (n.layer=2) |
var.e |
prior estimate matrix for experiemtnal variances (n.layer=2) |
var.t |
prior estimate matrix for total variances (n.layer=1) |
Author(s)
HyungJun Cho, PhD, and Jae K. Lee, PhD,
See Also
hem
,
hem.fdr
Examples
#Example 1: Two-layer HEM with EB prior specification
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)
#Example 2: One-layer HEM with EB prior specification
data(mubcp)
##construct a design matrix
cond <- c(rep(1,6),rep(2,5),rep(3,5),rep(4,5),rep(5,5))
ind <- c(1:6,rep((1:5),4))
design <- data.frame(cond,ind)
##normalization
mubcp.nor <- hem.preproc(mubcp)
##take a subset for a testing purpose;
##use all genes for a practical purpose
mubcp.nor <- mubcp.nor[1:1000,]
##estimate hyperparameters of variances by LPE
mubcp.eb <- hem.eb.prior(mubcp.nor, n.layer=1, design=design,
method.var.t="neb")
#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
mubcp.hem <- hem(mubcp.nor, n.layer=1, design=design, burn.ins=10, n.samples=30,
method.var.t="neb", var.t=mubcp.eb$var.t)
###NOTE: see help(hem.fdr) for examples with FDR evaluation
###NOTE: see help(hem) for examples without EB
[Package
HEM version 1.0.4
Index]