Version:	0.6
Date:	2024-11-27
Title:	Microbiome Mixture Analysis
Description:	Evaluate whether a microbiome sample is a mixture of two samples, by fitting a model for the number of read counts as a function of single nucleotide polymorphism (SNP) allele and the genotypes of two potential source samples. Lobo et al. (2021) <doi:10.1093/g3journal/jkab308>.
Author:	Karl W Broman [aut, cre]
Maintainer:	Karl W Broman <broman@wisc.edu>
Depends:	R (≥ 3.1.0)
Imports:	stats, parallel, numDeriv
Suggests:	knitr, rmarkdown, testthat, devtools, roxygen2
License:	MIT + file LICENSE
URL:	https://github.com/kbroman/mbmixture
BugReports:	https://github.com/kbroman/mbmixture/issues
VignetteBuilder:	knitr
LazyData:	true
Encoding:	UTF-8
ByteCompile:	true
RoxygenNote:	7.2.3
NeedsCompilation:	no
Packaged:	2024-11-27 22:51:22 UTC; kbroman
Repository:	CRAN
Date/Publication:	2024-11-27 23:20:02 UTC

Bootstrap to assess significance

Description

Perform a parametric bootstrap to assess whether there is significant evidence that a sample is a mixture.

Usage

bootstrapNull(
  tab,
  n_rep = 1000,
  interval = c(0, 1),
  tol = 0.000001,
  check_boundary = TRUE,
  cores = 1,
  return_raw = TRUE
)

Arguments

Value

If return_raw=FALSE, a single numeric value (the p-value).If return_raw=TRUE, a vector of length n_rep with the LRT statistics from each bootstrap replicate.

See Also

bootstrapSE()

Examples

data(mbmixdata)
# just 100 bootstrap replicates, as an illustration
bootstrapNull(mbmixdata, n_rep=100)

Bootstrap to get standard errors

Description

Perform a parametric bootstrap to get estimated standard errors.

Usage

bootstrapSE(
  tab,
  n_rep = 1000,
  interval = c(0, 1),
  tol = 0.000001,
  check_boundary = FALSE,
  cores = 1,
  return_raw = FALSE
)

Arguments

Value

If return_raw=FALSE, a vector of two standard errors. If return_raw=TRUE, an matrix of size n_rep x 2 with the detailed bootstrap results.

See Also

bootstrapNull()

Examples

data(mbmixdata)
# just 100 bootstrap replicates, as an illustration
bootstrapSE(mbmixdata, n_rep=100)

log likelihood function for microbiome mixture

Description

Calculate log likelihood function for microbiome sample mixture model at particular values of p and e.

Usage

mbmix_loglik(tab, p, e = 0)

Arguments

Value

The log likelihood evaluated at p and e.

Examples

data(mbmixdata)
mbmix_loglik(mbmixdata, p=0.74, e=0.002)

Example dataset for mbmixture package

Description

Example dataset for mbmixture package.

Usage

data(mbmixdata)

Format

Dataset of read counts as 3d array of size 3x3x2, genotype in first sample x genotype in second sample x allele in read.

Examples

data(mbmixdata)
mle_pe(mbmixdata)

MLE of e for fixed p

Description

Calculate the MLE of the sequencing error rate e for a fixed value of the contaminant probability p.

Usage

mle_e(
  tab,
  p = 0.05,
  interval = c(0, 1),
  tol = 0.000001,
  check_boundary = FALSE
)

Arguments

Value

A single numeric value, the MLE of e, with the log likelihood as an attribute.

Examples

data(mbmixdata)
mle_e(mbmixdata, p=0.74)

MLE of p for fixed e

Description

Calculate the MLE of the contaminant probability p for a fixed value of the sequencing error rate e.

Usage

mle_p(
  tab,
  e = 0.002,
  interval = c(0, 1),
  tol = 0.000001,
  check_boundary = FALSE
)

Arguments

Value

A single numeric value, the MLE of p, with the log likelihood as an attribute.

Examples

data(mbmixdata)
mle_p(mbmixdata, e=0.002)

Find MLEs for microbiome mixture

Description

Find joint MLEs of p and e for microbiome mixture model

Usage

mle_pe(
  tab,
  interval = c(0, 1),
  tol = 0.000001,
  check_boundary = FALSE,
  SE = FALSE
)

Arguments

Value

A vector containing the estimates of p and e along with the evaluated log likelihood and likelihood ratio test statistics for the hypotheses p=0 and p=1.

Examples

data(mbmixdata)
mle_pe(mbmixdata)