getExpressionEstimates {AnaCoDa} | R Documentation |
Posterior estimates for the phi value of specified genes
getExpressionEstimates(parameter, gene.index, samples, quantiles = c(0.025, 0.975))
parameter |
on object created by |
gene.index |
a integer or vector of integers representing the gene(s) of interesst. |
samples |
number of samples for the posterior estimate |
quantiles |
vector of quantiles, (default: c(0.025, 0.975)) |
The returned vector is unnamed as gene ids are only stored in the genome
object,
but the gene.index
vector can be used to match the assignment to the genome.
returns a vector with the mixture assignment of each gene corresbonding to gene.index
in the same order as the genome.
genome_file <- system.file("extdata", "genome.fasta", package = "AnaCoDa") genome <- initializeGenomeObject(file = genome_file) sphi_init <- c(1,1) numMixtures <- 2 geneAssignment <- c(rep(1,floor(length(genome)/2)),rep(2,ceiling(length(genome)/2))) parameter <- initializeParameterObject(genome = genome, sphi = sphi_init, num.mixtures = numMixtures, gene.assignment = geneAssignment, mixture.definition = "allUnique") model <- initializeModelObject(parameter = parameter, model = "ROC") samples <- 2500 thinning <- 50 adaptiveWidth <- 25 mcmc <- initializeMCMCObject(samples = samples, thinning = thinning, adaptive.width=adaptiveWidth, est.expression=TRUE, est.csp=TRUE, est.hyper=TRUE, est.mix = TRUE) divergence.iteration <- 10 ## Not run: runMCMC(mcmc = mcmc, genome = genome, model = model, ncores = 4, divergence.iteration = divergence.iteration) # get the estimated expression values for all genes based on the mixture # they are assigned to at each step estimatedExpression <- getExpressionEstimates(parameter, 1:length(genome), 1000) ## End(Not run)