calculateDMN {mia}

R Documentation

Dirichlet-Multinomial Mixture Model: Machine Learning for Microbiome Data

Description

These functions are accessors for functions implemented in the DirichletMultinomial package

Usage

calculateDMN(x, ...)

## S4 method for signature 'ANY'
calculateDMN(
  x,
  k = 1,
  BPPARAM = SerialParam(),
  seed = runif(1, 0, .Machine$integer.max),
  ...
)

## S4 method for signature 'SummarizedExperiment'
calculateDMN(
  x,
  abund_values = exprs_values,
  exprs_values = "counts",
  transposed = FALSE,
  ...
)

runDMN(x, name = "DMN", ...)

getDMN(x, name = "DMN", ...)

## S4 method for signature 'SummarizedExperiment'
getDMN(x, name = "DMN")

bestDMNFit(x, name = "DMN", type = c("laplace", "AIC", "BIC"), ...)

## S4 method for signature 'SummarizedExperiment'
bestDMNFit(x, name = "DMN", type = c("laplace", "AIC", "BIC"))

getBestDMNFit(x, name = "DMN", type = c("laplace", "AIC", "BIC"), ...)

## S4 method for signature 'SummarizedExperiment'
getBestDMNFit(x, name = "DMN", type = c("laplace", "AIC", "BIC"))

calculateDMNgroup(x, ...)

## S4 method for signature 'ANY'
calculateDMNgroup(
  x,
  variable,
  k = 1,
  seed = runif(1, 0, .Machine$integer.max),
  ...
)

## S4 method for signature 'SummarizedExperiment'
calculateDMNgroup(
  x,
  variable,
  abund_values = exprs_values,
  exprs_values = "counts",
  transposed = FALSE,
  ...
)

performDMNgroupCV(x, ...)

## S4 method for signature 'ANY'
performDMNgroupCV(
  x,
  variable,
  k = 1,
  seed = runif(1, 0, .Machine$integer.max),
  ...
)

## S4 method for signature 'SummarizedExperiment'
performDMNgroupCV(
  x,
  variable,
  abund_values = exprs_values,
  exprs_values = "counts",
  transposed = FALSE,
  ...
)

Arguments

x	a numeric matrix with samples as rows or a SummarizedExperiment object.
...	optional arguments not used.
k	the number of Dirichlet components to fit. See dmn
BPPARAM	A BiocParallelParam object specifying whether the UniFrac calculation should be parallelized.
seed	random number seed. See dmn
abund_values	a single character value for specifying which assay to use for calculation.
exprs_values	a single character value for specifying which assay to use for calculation. (Please use abund_values instead. At some point exprs_values will be disabled.)
transposed	Logical scalar, is x transposed with samples in rows?
name	the name to store the result in metadata
type	the type of measure used for the goodness of fit. One of ‘laplace’, ‘AIC’ or ‘BIC’.
variable	a variable from colData to use as a grouping variable. Must be a character of factor.

Value

calculateDMN and getDMN return a list of DMN objects, one element for each value of k provided.

bestDMNFit returns the index for the best fit and getBestDMNFit returns a single DMN object.

calculateDMNgroup returns a DMNGroup object

performDMNgroupCV returns a data.frame

See Also

DMN-class, DMNGroup-class, dmn, dmngroup, cvdmngroup , accessors for DMN objects

Examples

fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv")
counts <- as.matrix(read.csv(fl, row.names=1))
fl <- system.file(package="DirichletMultinomial", "extdata", "TwinStudy.t")
pheno0 <- scan(fl)
lvls <- c("Lean", "Obese", "Overwt")
pheno <- factor(lvls[pheno0 + 1], levels=lvls)
colData <- DataFrame(pheno = pheno)

se <- SummarizedExperiment(assays = list(counts = counts),
                           colData = colData)


#
dmn <- calculateDMN(se)
dmn[[1L]]

# since this take a bit of resources to calculate for k > 1, the data is
# loaded
## Not run: 
se <- runDMN(se, name = "DMN", k = 1:7)

## End(Not run)
data(dmn_se)
names(metadata(dmn_se))

# return a list of DMN objects
getDMN(dmn_se)
# return, which objects fits best
bestDMNFit(dmn_se, type = "laplace")
# return the model, which fits best
getBestDMNFit(dmn_se, type = "laplace")

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