fit.model {rama}R Documentation

Robust estimation of microarray intensities with replicates

Description

Estimate the log transformed intensities of each sample of a replicated microarray experiment. The estimation is done via Hiearchical Bayesian Modeling.

Usage

fit.model(sample1,sample2,B=1000,min.iter=0,batch=10,shift=NULL,mcmc.obj=NULL,dye.swap=FALSE,nb.col1=NULL,all.out=TRUE)
     

Arguments

sample1 The matrix of intensity from the sample 1. Each row corresponds to a different gene.
sample2 The matrix of intensity from the sample 2. Each row corresponds to a different gene.
B The number of iteration used the MCMC algorithm.
min.iter The length of the burn-in period in the MCMC algorithm.min.iter should be less than B.
batch The thinning value to be used in the MCMC. Only every batch-th iteration will be stored.
mcmc.obj An object of type mcmc, as returned by fit.model. mcmc.obj is used to initialized the MCMC. If no mcmc.obj, the MCMC is initialized to the least squares estimates.
shift The shift to be used in the log transformation. If shift=NULL is specified (default), it is estimated using est.shift
dye.swap A logical value indicating if the experiment was a dye swap experiment.
nb.col1 An integer value corresponding to the number of arrays (columns) in the first group of the dye swap experiment. In other words, the number of replicates before the dyes have been swaped.
all.out A logical value indicating if all the parameters should be outputted. If all.out is FALSE, only the posterior mean is outputted. This could be used to save memory.

Details

The function fits a hierarchical Bayesian model for robust estimation of cDNA microarray intensities. Our model addresses classical issues such as design effects, normalization and transformation. Outliers are modeled explicitly using a t-distribution. Parameter estimation is carried out using Markov Chain Monte Carlo.

Value

An object of type mcmc containing the sampled values from the posterior distribution.

mu A vector containing the sampled values from mu, the baseline intensity.
alpha2 A vector containing the sampled values from alpha2, the sample effect.
beta2 A vector containing the sampled values from beta2, the dye effect.
delta22 A vector containing the sampled values from delta_22, the dye*sample interaction.
eta A matrix, each row contains the sampled values from the corresponding array effect.
gamma1 A matrix, each row contains the sampled values from the corresponding gene effect in sample 1.
gamma2 A matrix, each row contains the sampled values from the corresponding gene effect in sample 1.
lambda.gamma1 A vector containing the sampled values for the precision of the gene effect prior in sample 1.
lambda.gamma2 A vector containing the sampled values for the precision of the gene effect prior in sample 2.
rho A vector containing the sampled values from between sample correlation coefficient rho
lambda_eps1 A matrix, each row contains the sampled values from the corresponding gene precision in sample 1.
lambda_eps2 A matrix, each row contains the sampled values from the corresponding gene precision in sample 2.
a.eps A vector containing the sampled values for the mean of the prior of the genes precision.
b.eps A vector containing the sampled values for the variance of the prior of the genes precision.
w A matrix, each element (i,j) correspond to the posterior mean of the sampled weights of replicate j in gene i.To save memory, we only store the posterior means of the weigths.
shift The value of the shift.

Author(s)

Raphael Gottardo

References

Robust Estimation of cDNA Microarray Intensities with Replicates Raphael Gottardo, Adrian E. Raftery, Ka Yee Yeung, and Roger Bumgarner Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322

See Also

est.shift

Examples

data(hiv)
mcmc.hiv<-fit.model(hiv[1:10,c(1:4)],hiv[1:10,c(5:8)],B=2000,min.iter=000,batch=1,shift=30,mcmc.obj=NULL,dye.swap=TRUE,nb.col1=2)

[Package rama version 1.0.1 Index]