combi: Perform model-based data integration
In combi: Compositional omics model based visual integration

Description Usage Arguments Details Value Examples

Perform model-based data integration

combi(data, M = 2L, covariates = NULL, distributions, compositional,
  maxIt = 300L, tol = 0.001, verbose = FALSE, prevCutOff = 0.95,
  minFraction = 0.1, logTransformGaussian = TRUE, confounders = NULL,
  compositionalConf = rep(FALSE, length(data)),
  nleq.control = list(maxit = 1000L, cndtol = 1e-16), record = TRUE,
  weights = NULL, fTol = 1e-05, meanVarFit = "spline",
  maxFeats = 2000, dispFreq = 10L, allowMissingness = FALSE,
  biasReduction = TRUE, maxItFeat = 20L)

`data`	A list of data objects with the same number of samples. See details.
`M`	the required dimension of the fit, a non-negative integer
`covariates`	a dataframe of n samples with sample-specific variables.
`distributions`	a character vector describing which distributional assumption should be used. See details.
`compositional`	A logical vector with the same length as "data", indicating if the datasets should be treated as compositional
`maxIt`	an integer, the maximum number of iterations
`tol`	A small scalar, the convergence tolerance
`verbose`	Logical. Should verbose output be printed to the console?
`prevCutOff`	a scalar, the prevalance cutoff for the trimming.
`minFraction`	a scalar, each taxon's total abundance should equal at least the number of samples n times minFraction, otherwise it is trimmed.
`logTransformGaussian`	A boolean, should the gaussian data be logtransformed, i.e. are they log-normal?
`confounders`	A dataframe or a list of dataframes with the same length as data. In the former case the same dataframe is used for conditioning, In the latter case each view has its own conditioning variables (or NULL).
`compositionalConf`	A logical vector with the same length as "data", indicating if the datasets should be treated as compositional when correcting for confounders. Numerical problems may occur when set to TRUE
`nleq.control`	A list of arguments to the nleqslv function
`record`	A boolean, should intermediate estimates be stored? Can be useful to check convergence
`weights`	A character string, either 'marginal' or 'uniform', indicating rrhow the feature parameters should be weighted in the normalization
`fTol`	The tolerance for solving the estimating equations
`meanVarFit`	The type of mean variance fit, see details
`maxFeats`	The maximal number of features for a Newton-Raphson procedure to be feasible
`dispFreq`	An integer, the period after which the variances should be reestimated
`allowMissingness`	A boolean, should NA values be allowed?
`biasReduction`	A boolean, should bias reduction be applied to allow for confounder correction in groups with all zeroes? Not guaranteed to work
`maxItFeat`	Integers, the maximum allowed number of iterations in the estimation of the feature parameters

Data can be provided as raw matrices with features in the columns, or as phyloseq, SummarizedExperiment or ExpressionSet objects. Estimation of independence model and view wise parameters can be parametrized. See ?BiocParallel::bplapply and ?BiocParallel::register. meanVarFit = "spline" yields a cubic spline fit for the abundance-variance trend, "cubic" gives a third degree polynomial. Both converge to the diagonal line with slope 1 for small means. Distribution can be either "quasi" for quasi likelihood or "gaussian" for Gaussian data

An object of the "combi" class, containing all information on the data integration and fitting procedure

data(Zhang)
#The method works on several datasets at once, and simply is not very fast.
#Hence the "Not run" statement

#Unconstrained
microMetaboInt = combi(
list("microbiome" = zhangMicrobio, "metabolomics" = zhangMetabo),
distributions = c("quasi", "gaussian"), compositional = c(TRUE, FALSE),
logTransformGaussian = FALSE, verbose = TRUE)
#Constrained
microMetaboIntConstr = combi(
    list("microbiome" = zhangMicrobio, "metabolomics" = zhangMetabo),
    distributions = c("quasi", "gaussian"), compositional = c(TRUE, FALSE),
    logTransformGaussian = FALSE, covariates = zhangMetavars, verbose = TRUE)