| 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
tab |
Dataset of read counts as 3d array of size 3x3x2, genotype in first sample x genotype in second sample x allele in read. |
n_rep |
Number of bootstrap replicates |
interval |
Interval to which each parameter should be constrained |
tol |
Tolerance for convergence |
check_boundary |
If TRUE, explicitly check the boundaries of |
cores |
Number of CPU cores to use, for parallel calculations.
(If |
return_raw |
If TRUE, return the raw results. If FALSE, just return the p-value.
Unlink |
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
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
tab |
Dataset of read counts as 3d array of size 3x3x2, genotype in first sample x genotype in second sample x allele in read. |
n_rep |
Number of bootstrap replicates |
interval |
Interval to which each parameter should be constrained |
tol |
Tolerance for convergence |
check_boundary |
If TRUE, explicitly check the boundaries of |
cores |
Number of CPU cores to use, for parallel calculations.
(If |
return_raw |
If TRUE, return the raw results. If FALSE, just return the estimated standard errors. |
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
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
tab |
Dataset of read counts as 3d array of size 3x3x2, genotype in first sample x genotype in second sample x allele in read. |
p |
Contaminant probability (proportion of mixture coming from the second sample). |
e |
Sequencing error rate. |
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
tab |
Dataset of read counts as 3d array of size 3x3x2, genotype in first sample x genotype in second sample x allele in read. |
p |
Assumed value for the contaminant probability |
interval |
Interval to which each parameter should be constrained |
tol |
Tolerance for convergence |
check_boundary |
If TRUE, explicitly check the boundaries of |
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
tab |
Dataset of read counts as 3d array of size 3x3x2, genotype in first sample x genotype in second sample x allele in read. |
e |
Assumed value for the sequencing error rate |
interval |
Interval to which each parameter should be constrained |
tol |
Tolerance for convergence |
check_boundary |
If TRUE, explicitly check the boundaries of |
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
tab |
Dataset of read counts as 3d array of size 3x3x2, genotype in first sample x genotype in second sample x allele in read. |
interval |
Interval to which each parameter should be constrained |
tol |
Tolerance for convergence |
check_boundary |
If TRUE, explicitly check the boundaries of |
SE |
If TRUE, get estimated standard errors. |
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)