bgx (version 1.38.0)

bgx: Fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data

Description

'bgx' estimates Bayesian Gene eXpression (BGX) measures from an AffyBatch object.

'standalone.bgx' creates various files needed by the bgx standalone binary and places them in a directory. One of these files is 'infile.txt'. In order to run standalone BGX, compile it and run 'bgx ' from the command line.

Usage

bgx(aData, samplesets = NULL, genes = NULL, genesToWatch = NULL, burnin = 8192, iter = 16384, output = c("minimal","trace","all"),  probeAff = TRUE, probecat_threshold = 100, adaptive = TRUE, rundir = ".")
standalone.bgx(aData, samplesets = NULL, genes = NULL, genesToWatch = NULL, burnin = 8192, iter = 16384, output = c("minimal", "trace", "all"), probeAff = TRUE, probecat_threshold = 100, adaptive = TRUE, batch_size = 50, optimalAR = 0.44, inputdir = "input")

Arguments

aData

An AffyBatch object.

samplesets

A numeric vector specifying which condition each array belongs to. E.g. if samplesets=c(2,2), then the first two replicates belong to one condition and the last two replicates belong to another condition. If NULL, each array is assumed to belong to a different condition. If the aData object contains information about the experiment design in its phenoData slot, this argument is not required.

genes

A numeric vector specifying which genes to analyse. If NULL, all genes are analysed.

genesToWatch

A numeric vector specifying which genes to monitor closely amongst those chosen to be analysed (see below for details).

burnin

Number of burn-in iterations.

iter

Number of post burn-in iterations.

output

One of "minimal", "trace" or "all". See below for details.

probeAff

Stratify the mean (lambda) of the cross-hybridisation parameter (H) by categories according to probe-level sequence information.

probecat_threshold

Minimum amount of probes per probe affinity category.

adaptive

Adapt the variance of the proposals for Metropolis Hastings objects (that is: S, H, Lambda, Eta, Sigma and Mu).

batch_size

Size of batches for calculating acceptance ratios and updating jumps.

optimalAR

Optimal acceptance ratio.

rundir

The directory in which to save the output runs.

inputdir

The name of the directory in which to place the input files for the standalone binary.

Value

'bgx' returns an ExpressionSet object containing gene expression information for each gene under each condition (not each replicate).'standalone.bgx' returns the path to the BGX input files.

Details

• genesToWatchSpecify the subset of genes for which thinned samples from the full posterior distributions of log(S+1) (x) and log(H+1) (y) are collected.
• outputOutput the following to disk:
  • "minimal"The gene expression measure (muave), thinned samples from the full posterior distributions of mu (mu.[1..c]), where 'c' is the number of conditions, the integrated autocorrelation time (IACT) and the Markov chain Monte Carlo Standard Error (MCSE) for each gene under each condition. Note that the IACT and MCSE are calculated from the thinned samples of mu.
  • "trace"The same as "minimal" plus thinned samples from the full posterior distributions of sigma2 (sigma2.[1..c]), lambda (lambda.[1..s]), eta2 (eta2), phi (phi) and tau2 (tau2), where 's' is the number of samples. If there are probes with unknown sequences, output a thinned trace of their categorisation.
  • "all"The same as "trace" plus acceptance ratios for S (sacc), H (hacc), mu (muacc), sigma (sigmaacc), eta (etaacc) and lambda (lambdasacc).

References

Turro, E., Bochkina, N., Hein, A., Richardson, S. (2007) BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips. BMC Bioinformatics 2007, 8:439. Hein, A., Richardson, S. (2006) A powerful method for detecting differentially expressed genes from GeneChip arrays that does not require replicates. BMC Bioinformatics 2006, 7:353. Hein, A., Richardson, S., Causton, H., Ambler, G., Green., P. (2005) BGX: a fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data. Biostatistics, 6, 3, pp. 349-373.

Hekstra, D., Taussig, A. R., Magnasco, M., and Naef, F. (2003) Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide array. Nucleic Acids Research, 31. 1962-1968.

G.O. Roberts, J.S. Rosenthal (September, 2006) Examples of Adaptive MCMC.

Examples

  # This example requires the 'affydata' and 'hgu95av2cdf' packages 
  if(require(affydata) && require(hgu95av2cdf)) {
    data(Dilution)
    eset <- bgx(Dilution, samplesets=c(2,2), probeAff=FALSE, burnin=4096, iter=8192,
      genes=c(12500:12599), output="all", rundir=file.path(tempdir()))
  }