Package 'negenes'

Number of insertion sites in each gene in M tb CDC1551

Description

Number of insertion sites in the initial 80% of each gene in the Mycobacterium tuberculosis CDC1551 genome.

Format

A matrix with two columns. Each row corresponds to a gene. (The row names are the MT numbers of the genes.) The element in the first column is the number of transposon insertion sites in the initial 80% that appear in the corresponding gene and in no other gene. The element in the second column is the number of transposon insertion sites in the initial 80% of both that gene and the following gene. There are 4204 rows; the 46 genes with no such site are not included.

Source

https://www.jcvi.org/ (formerly TIGR)

References

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. https://www.biostat.wisc.edu/~kbroman/publications/ms0220.pdf

See Also

negenes(), sim.mutants()

Examples

## Not run: 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
output <- negenes(Mtb80[,1], counts[,1], Mtb80[,2], counts[,2])
## End(Not run)

---

Estimate the number of essential genes in a genome

Description

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. (See the technical report cited below.)

Usage

negenes(
  n.sites,
  counts,
  n.sites2 = NULL,
  counts2 = NULL,
  n.mcmc = 5000,
  skip = 49,
  burnin = 500,
  startp = 1,
  trace = TRUE,
  calc.prob = FALSE,
  return.output = FALSE
)

Arguments

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. If NULL, assume all are 0.
counts2	A vector specfying the number of mutants shared by adjacent gene (analogous to n.sites2). The ith element is the number of mutants at sites shared by genes i and i+1. The last element is for sites shared by genes N and 1. If NULL, assume all are 0.
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.

Value

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.

Author(s)

Karl W Broman, [email protected]

References

• 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. https://www.biostat.wisc.edu/~kbroman/publications/ms0220.pdf

• Lamichhane et al. (2003) A post-genomic method for predicting essential genes at subsaturation levels of mutagenesis: application to Mycobacterium, tuberculosis. Proc Natl Acad Sci USA 100:7213-7218 doi:10.1073/pnas.1231432100

See Also

sim.mutants(), Mtb80()

Examples

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
## Not run: output <- negenes(Mtb80[,1], counts[,1], Mtb80[,2], counts[,2])

# run the Gibbs sampler, returning the detailed output
## Not run: output2 <- negenes(Mtb80[,1], counts[,1], Mtb80[,2], counts[,2], return=TRUE)

---

Simulate data for a random transposon mutagenesis experiment

Description

Simulate data for a random transposon mutagenesis experiment.

Usage

sim.mutants(n.sites, essential, n.sites2 = NULL, n.mutants)

Arguments

n.sites	A vector specifying the number of transposon insertion sites in each gene. All elements must by strictly positive.
essential	A vector containing 1's (indicating that the corresponding gene is essential) and 0's (indicating that the corresponding gene is not essential). Must be the same length as n.sites.
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. If missing, these are assumed to be all 0.
n.mutants	Number of mutants to simulate.

Value

If n.sites2 is missing or contains all 0's, a vector is returned containing the number of mutants observed for each gene.

If n.sites2 is not missing and has some positive entries, a matrix with two columns is returned. The first column contains the number of mutants observed for each gene alone; the second column contains the number of mutants observed shared by adjacent genes.

Author(s)

Karl W Broman, [email protected]

References

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. https://www.biostat.wisc.edu/~kbroman/publications/ms0220.pdf

See Also

negenes(), Mtb80()

Examples

## Not run: 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
output <- negenes(Mtb80[,1], counts[,1], Mtb80[,2], counts[,2])
## End(Not run)

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