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Nonparametric Microbial Interdependence Test (NMIT)

Source: R/NMIT.R
NMIT.Rd

An R-based implementation of the NMIT, a multivariate distance-based test for group comparisons of microbial temporal interdependence. The NMIT test provides a comprehensive way to evaluate the association between key phenotypic variables and microbial interdependence. This function is recommended for use after a filtering step using filter_MAE. Note, the "ComplexHeatmap" package is required to use the plotting features of the function. The function requires the "vegan" package.

Usage

NMIT(
  dat,
  unit_var,
  fixed_cov,
  covariate_time,
  method = "kendall",
  dist_type = "F",
  heatmap = TRUE,
  classify = FALSE,
  fill_na = 0,
  ...
)

Arguments

dat

A MultiAssayExperiment object specially formatted as an animalcules output.

unit_var

a numeric vector of subject.

fixed_cov

A character vector of the names of covariates of interest found in dat.

covariate_time

Character string giving the name of the discrete time-based covariate in the metadata to group abundances by.

method

an option of the correlation method ("pearson", "kendall", "spearman"). The default method is "kendall".

dist_type

A character string specifying the type of matrix norm to be computed. The default is "F". * "M" or "m" specifies the maximum modulus of all the elements in x; * "O", "o" or "1" specifies the one norm, (maximum absolute column sum); * "I" or "i" specifies the infinity norm (maximum absolute row sum); * "F" or "f" specifies the Frobenius norm (the Euclidean norm of x treated as if it were a vector)

heatmap

A logical value indicating whether to draw heatmap. The default is TRUE.

classify

A logical value indicating whether to draw a classifier tree. The default is FALSE.

fill_na

A number between 0 and 1 to fill NA values. The default value is 0.

...

Additional arguments to be passed to ComplexHeatmap::Heatmap().

Value

This function returns an analysis of variance (ANOVA) table showing sources of variation, degrees of freedom, sequential sums of squares, mean squares, F statistics, partial R-squared and P values, based on 999 permutations.

Author

Yilong Zhang, Huilin Li, Aubrey Odom

Examples

dat <- system.file("extdata/MAE_small.RDS", package = "LegATo") |> readRDS()
NMIT(dat, unit_var = "Subject", fixed_cov = "Group", covariate_time = "Month")

#> NULL