fit.model: Robust estimation of microarray intensities with replicates
In rama: Robust Analysis of MicroArrays

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/fit.model.R

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

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fit.model(sample1,sample2,B=1000,min.iter=0,batch=10,shift=NULL,mcmc.obj=NULL,dye.swap=FALSE,nb.col1=NULL,all.out=FALSE,ci=0.95,
verbose=FALSE)

`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.
`ci`	A number between 0 and 1 corresponding to the level used when computing log ratio credible intervals. If `all.out` is FALSE, this option is ignored.
`verbose`	A logical value indicating if the current MCMC iteration number should be printed out.

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.

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.
`q.low`	A vector containing the lower bounds for the log ratio credible intervals, i.e. the credible intervals for gamma1-gamma2.
`q.up`	A vector containing the upper bounds for the log ratio credible intervals, i.e. the credible intervals for gamma1-gamma2.
`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.

Raphael Gottardo

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

est.shift