Package 'MIDASim'

IBD microbiome dataset

Description

A filtered microbiome dataset of patients with IBD(Inflammatory Bowel Disease) in Human Microbiome Project 2 (HMP2).

Usage

data(count.ibd)

Format

An object of class matrix (inherits from array) with 146 rows and 614 columns.

References

Lloyd-Price, J., Arze, C., Ananthakrishnan, A.N. *et al*. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. *Nature* 569, 655–662 (2019). https://doi.org/10.1038/s41586-019-1237-9.

Examples

data(count.ibd)

MIDASim.setup(otu.tab = count.ibd, mode = "nonparametric")

---

MOMS-PI microbiome dataset

Description

A filtered microbiome dataset of Multi-Omic Microbiome Study-Pregnancy Initiative (MOMS-PI) in Human Microbiome Project 2 (HMP2).

Usage

data(count.vaginal)

Format

An object of class matrix (inherits from array) with 517 rows and 1146 columns.

References

Fettweis, J.M., Serrano, M.G., Brooks, J.P. et al. The vaginal microbiome and preterm birth. Nat Med 25, 1012–1021 (2019). https://doi.org/10.1038/s41591-019-0450-2

Examples

data(count.vaginal)

MIDASim.setup(otu.tab = count.vaginal, mode = "nonparametric")

---

Simulating Realistic Microbiome Data using MIDASim

Description

Generate microbiome datasets using parameters from MIDASim.modify.

Usage

MIDASim(fitted.modified, only.rel = FALSE)

Arguments

fitted.modified	Output from MIDASim.modify.
only.rel	A logical indicating whether to only simulate relative- abundance data. If TRUE, then the count data will not be generated. Defaults to FALSE.

Value

Returns a list that has components:

sim_01	Matrix of simulated presence-absence data
sim_rel	Matrix of simulated relative-abundance data
sim_count	Matrix of simulated count data

Author(s)

Mengyu He

Examples

data("throat.otu.tab")
  otu.tab = throat.otu.tab[,colSums(throat.otu.tab>0)>1]

  fitted = MIDASim.setup(otu.tab)
  fitted.modified = MIDASim.modify(fitted)
  sim = MIDASim(fitted.modified, only.rel = FALSE)

---

Modifying MIDASim model

Description

MIDASim.modify() modifies the fitted MIDASim.setup model according to user specification that one or multiple of the following characteristics, such as the library sizes, taxa relative abundances, location parameters of the parametric model can be changed. This is useful if the users wants to introduce an 'effect' in simulation studies.

Usage

MIDASim.modify(
  fitted,
  lib.size = NULL,
  mean.rel.abund = NULL,
  gengamma.mu = NULL,
  sample.1.prop = NULL,
  taxa.1.prop = NULL,
  individual.rel.abund = NULL,
  ...
)

Arguments

fitted	Output from MIDASim.setup.
lib.size	Numeric vector of pre-specified library sizes (length should be equal to n.sample if specified). In nonparametric mode, if lib.size is specified, both taxa.1.prop and sample.1.prop should be specified.
mean.rel.abund	Numeric vector of specified mean relative abundances for taxa. Length should be equal to n.taxa in fitted.
gengamma.mu	Numeric vector of specified location parameters for the parametric model (generalized gamma model). Specify either mean.rel.abund or gengamma.mu, not both. Length should be equal to n.taxa in fitted. See Details. This argument is only applicable in parametric mode.
sample.1.prop	Numeric vector of specified proportion of non-zeros for subjects (the length should be equal to n.sample in fitted). This argument is only applicable in nonparametric mode.
taxa.1.prop	Numeric vector of specified proportion of non-zeros for taxa (the length should be equal to n.taxa in fitted). This argument is only applicable in nonparametric mode.
individual.rel.abund	Numeric matrix of expected relative abundances with n.sample rows and n.taxa columns (rows should sum to 1). Provides subject‑specific mean compositions and therefore overrides mean.rel.abund and gengamma.mu. Only applicable in parametric mode.
...	Additional arguments. If SCAM model is chosen for parameter changes under the non-parametric mode, specify SCAM = T.

Details

The parametric model in MIDASim is a location-scale model, specifically, a generalized gamma model for relative abundances $\pi$ of a taxon. Denote $t = 1/\pi$. The generalized gamma distribution for $t$ is chosen so that

$ln(t)\ =\ \mu\ +\ \sigma \cdot w$

where $w$ follows a log gamma distribution with a shape parameter $1/Q$. MIDASim fits the model to the template data and estimates parameters $\mu$, $\sigma$ and $Q$ by matching the first two moments of $\pi$ and maximizing the likelihood.

Value

Returns an updated list with different elements depending on the value of fitted$mode:

n.sample	Target sample size in the simulation.
lib.size	Target library sizes in the simulation.
taxa.1.prop	Updated proportions of non-zero values for each taxon.
sample.1.prop	Updated proportion of non-zero cells for each subject.
theta	Mean values of the multivariate normal distribution in generating presence-absence data.
eta	Adjustment to be applied to samples in generating presence- absence data.

Author(s)

Mengyu He

Examples

data("throat.otu.tab")
  otu.tab = throat.otu.tab[,colSums(throat.otu.tab>0)>1]

  fitted = MIDASim.setup(otu.tab, mode = 'parametric')

  # modify library sizes
  fitted.modified <- MIDASim.modify(fitted,
                                    lib.size = sample(fitted$lib.size, 2*nrow(otu.tab),
                                                    replace = TRUE) )

  # modify mean relative abundances
  fitted.modified <- MIDASim.modify(fitted,
                                    mean.rel.abund = fitted$mean.rel.abund * runif(fitted$n.taxa))

---

Fitting MIDAS model to microbiome data

Description

Midas.setup estimates parameters from a template microbiome count dataset for downstream data simulation.

Usage

MIDASim.setup(otu.tab, n.break.ties = 100, mode = "nonparametric")

Arguments

otu.tab	Numeric matrix of template microbiome count dataset. Rows are samples, columns are taxa.
n.break.ties	Number of replicates to break ties when ranking relative abundances. Defaults to 100.
mode	A character indicating the modeling approach for relative abundances. If 'parametric', a parametric model involving fitting a generalized gamma distribution is used. If 'nonparametric', the nonparametric approach involving quantile matching is applied. Note that a parametric model is required if library sizes or characteristics of taxa will be modified. Defaults to 'nonparametric'.

Value

Returns a list that has components:

mat01	Presence-absence matrix of the template data.
lib.size	Observed library sizes of the template data.
n.taxa	Number of taxa in the template data.
n.sample	Sample size in the template data.
ids	Taxa ids present in all samples in the template.
tetra.corr	Estimated tetrachoric correlation of the presence-absence matrix of the template.
corr.rel.corrected	Estimated Pearson correlation of relative abundances, transformed from Spearman's rank correlation.
sample.1.prop	Proportion of non-zero cells for each subject.
taxa.1.prop	Proportion of non-zeros for each taxon.
mean.rel.abund	Observed mean relative abundances of each taxon.
rel.abund.1	Observed non-zero relative abundances of each taxon.
taxa.names	Names of taxa in the template.

Author(s)

Mengyu He

Examples

data("throat.otu.tab")
  otu.tab = throat.otu.tab[,colSums(throat.otu.tab>0)>1]

  # use nonparametric model
  fitted = MIDASim.setup(otu.tab)

  # use parametric model
  fitted = MIDASim.setup(otu.tab, mode = 'parametric')

---

throat microbiome dataset

Description

A microbiome dataset of 60 subjects with 856 OTUs. The data were collected from right and left nasopharynx and oropharynx region.

Usage

data(throat.otu.tab)

Format

An object of class data.frame with 60 rows and 856 columns.

References

Charlson, E. S., Chen, J., Custers-Allen, R., Bittinger, K., Li, H., Sinha, R., Hwang, J., Bushman, F. D., & Collman, R. G. (2010). Disordered microbial communities in the upper respiratory tract of cigarette smokers. PloS one, 5(12), e15216. https://doi.org/10.1371/journal.pone.0015216

Examples

data(throat.otu.tab)

MIDASim.setup(otu.tab = throat.otu.tab, mode = "nonparametric")

---