hem: Heterogeneous Error Model for Identification of Differential...

In HEM: Heterogeneous error model for identification of differentially 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

Author(s)

HyungJun Cho and Jae K. Lee

References

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

See Also

hem.eb.prior, hem.fdr

Examples

#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

HEM documentation built on Nov. 8, 2020, 5:57 p.m.