hem: Heterogeneous Error Model for Identification of Differential Expressed Genes Under Multiple Conditions

Description

Fits an error model with heterogeneous experimental and biological variances.

Usage

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)

Arguments

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.

Value

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

References

Cho, H. and Lee, J.K. (2004) Bayesian Hierarchical Error Model for Analysis of Gene Expression Data, Bioinformatics, 20: 2016-2025.

Examples

Run this code


#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