hem {HEM} | R Documentation |
Fits an error model with heterogeneous experimental and biological variances.
hem(dat, probe.ID=NULL, n.layer, design, burn.ins=1000, n.samples=3000, method.var.e="gam", method.var.b="gam", method.var.t="gam", var.e=NULL, var.b=NULL, var.t=NULL, var.g=1, var.c=1, var.r=1, alpha.e=3, beta.e=.1, alpha.b=3, beta.b=.1, alpha.t=3, beta.t=.2, n.digits=10, print.message.on.screen=TRUE)
dat |
data |
probe.ID |
a vector of probe set IDs |
n.layer |
number of layers; 1=one-layer EM, 2=two-layer EM |
design |
design matrix |
burn.ins |
number of burn-ins for MCMC |
n.samples |
number of samples for MCMC |
method.var.e |
prior specification method for experimental variance; "gam"=Gamma(alpha,beta), "peb"=parametric EB prior specification, "neb"=nonparametric EB prior specification |
method.var.b |
prior specification method for biological variance; "gam"=Gamma(alpha,beta), "peb"=parametric EB prior specification |
method.var.t |
prior specification method for total variance; "gam"=Gamma(alpha,beta), "peb"=parametric EB prior specification, "neb"=nonparametric EB prior specification |
var.e |
prior estimate matrix for experimental variance |
var.b |
prior estimate matrix for biological variance |
var.t |
prior estimate matrix for total variance |
var.g |
N(0, var.g); prior parameter for gene effect |
var.c |
N(0, var.c); prior parameter for condition effect |
var.r |
N(0, var.r); prior parameter for interaction effect of gene and condition |
alpha.e, beta.e |
Gamma(alpha.e,alpha.e); prior parameters for inverse of experimental variance |
alpha.b, beta.b |
Gamma(alpha.b,alpha.b); prior parameters for inverse of biological variance |
alpha.t, beta.t |
Gamma(alpha.b,alpha.b); prior parameters for inverse of total variance |
n.digits |
number of digits |
print.message.on.screen |
if TRUE, process status is shown on screen. |
n.gene |
numer of genes |
n.chip |
number of chips |
n.cond |
number of conditions |
design |
design matrix |
burn.ins |
number of burn-ins for MCMC |
n.samples |
number of samples for MCMC |
priors |
prior parameters |
m.mu |
estimated mean expression intensity for each gene under each condition |
m.x |
estimated unobserved expression intensity for each combination of genes, conditions, and individuals (n.layer=2) |
m.var.b |
estimated biological variances (n.layer=2) |
m.var.e |
estimated experiemental variances (n.layer=2) |
m.var.t |
estimated total variances (n.layer=1) |
H |
H-scores |
HyungJun Cho and Jae K. Lee
Cho, H. and Lee, J.K. (2004) Bayesian Hierarchical Error Model for Analysis of Gene Expression Data, Bioinformatics, 20: 2016-2025.
#Example 1: Two-layer HEM data(pbrain) ##construct a design matrix cond <- c(1,1,1,1,1,1,2,2,2,2,2,2) #condition ind <- c(1,1,2,2,3,3,1,1,2,2,3,3) #biological replicate rep <- c(1,2,1,2,1,2,1,2,1,2,1,2) #experimental replicate design <- data.frame(cond,ind,rep) ##normalization pbrain.nor <- hem.preproc(pbrain[,2:13]) ##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) ##print H-scores #pbrain.hem$H #Example 2: One-layer HEM data(mubcp) ##construct a design matrix cond <- c(rep(1,6),rep(2,5),rep(3,5),rep(4,5),rep(5,5)) ind <- c(1:6,rep((1:5),4)) design <- data.frame(cond,ind) ##construct a design matrix mubcp.nor <- hem.preproc(mubcp) #fit HEM with one layers of error #using the small numbers of burn-ins and MCMC samples for a testing purpose; #but increase the numbers for a practical purpose #mubcp.hem <- hem(mubcp.nor, n.layer=1,design=design, burn.ins=10, n.samples=30) ##print H-scores #mubcp.hem$H ###NOTE: Use 'hem.fdr' for FDR evaluation ###NOTE: Use 'hem.eb.prior' for Empirical Bayes (EB) prior sepecification ###NOTE: Use EB-HEM ('hem' after 'hem.eb.prior') for small data sets