negenes {negenes} | R Documentation |
Estimate, via a Gibbs sampler, the posterior distribution of the number of essential genes in a genome with data from a random transposon mutagenesis experiment.
negenes(n.sites, counts, n.sites2, counts2, n.mcmc=5000, skip=49, burnin=500, startp=1, trace=TRUE, calc.prob=FALSE, return.output=FALSE)
n.sites |
A vector specifying the number of transposon insertion sites in each gene (alone). All elements must by strictly positive. |
counts |
A vector specifying the number of mutants observed for
each gene (alone). Must be the same length as n.sites , and all
elements must be non-negative integers. |
n.sites2 |
A vector specfying the number of transposon insertion sites shared by adjacent genes. The ith element is the number of insertion sites shared by genes i and i+1. The last element is for sites shared by genes N and 1. |
counts2 |
A vector specfying the number of mutants shared by
adjacent gene (analogous to n.sites2 ). |
n.mcmc |
Number of Gibbs steps to perform. |
skip |
An integer; only save every skip + 1st step. |
burnin |
Number of initial Gibbs steps to run (output discarded). |
startp |
Initial proportion of genes for which no mutant was observed that will be assumed essential for the Gibbs sampler. (Genes for which a mutant was observed are assumed non-essential; other genes are assumed essential independent with this probability.) |
trace |
If TRUE, print iteration number occassionally. |
calc.prob |
If TRUE, return the log posterior probability (up to an additive constant) for each saved iteration. |
return.output |
If TRUE, include detailed Gibbs results in the output. |
See the technical report cited below.
A list with components n.essential
(containing the total number
of essential genes at each iteration of the Gibbs sampler)
summary
(a vector containing the estimated mean, SD, 2.5 percentile and
97.5 percentile of the posterior distribution of the number of
essential genes.
The next component, geneprob
, is a vector with one element for
each gene, containing the estimated posterior probability that each
gene is essential. These are Rao-Blackwellized estimates.
If the argument calc.prob
was true, there will also be a
component logprob
containing the log (base e) of the posterior
probability (up to an additive constant) at each Gibbs step.
If the argument return.output
was true, there will also be a
matrix with n.mcmc
/ (skip
+ 1) rows (corresponding to
the Gibbs steps) and a column for each gene The entries in the matrix
are either 0 (essential gene) or 1 (non-essential gene) according to
the state of that gene at that step in the Gibbs sampler.
Karl W Broman, kbroman@biostat.wisc.edu
Blades, N. J. and Broman, K. W. (2002) Estimating the number of essential genes in a genome by random transposon mutagenesis. Technical Report MS02-20, Department of Biostatistics, Johns Hopkins University, Baltimore, MD. http://www.biostat.biostat.wisc.edu/~kbroman/publications/ms0220.pdf
data(Mtb80) # simulate 44% of genes to be essential essential <- rep(0,nrow(Mtb80)) essential[sample(1:nrow(Mtb80),ceiling(nrow(Mtb80)*0.44))] <- 1 # simulate 759 mutants counts <- sim.mutants(Mtb80[,1], essential, Mtb80[,2], 759) # run the Gibbs sampler without returning detailed output output <- negenes(Mtb80[,1], counts[,1], Mtb80[,2], counts[,2]) # run the Gibbs sampler, returning the detailed output output2 <- negenes(Mtb80[,1], counts[,1], Mtb80[,2], counts[,2], return=TRUE)