hem.eb.prior {HEM} | R Documentation |
Estimates experimental and biological variances by LPE and resampling
hem.eb.prior(dat, n.layer, design, method.var.e="neb", method.var.b="peb", method.var.t="neb", rep=TRUE, baseline.var="LPE", p.remove=0, max.chip=4, q=0.01, B=25, n.digits=10, print.message.on.screen=TRUE)
dat |
data |
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 |
rep |
no replication if FALSE |
baseline.var |
baseline variance estimation method: LPE for replicated data and BLPE, PSE, or ASE for unreplicated data |
p.remove |
percent of removed rank-variance genes for BLPE |
max.chip |
maximum number of chips to estimate errors |
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. |
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) |
HyungJun Cho and Jae K. Lee
#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)