calculateDMN: Dirichlet-Multinomial Mixture Model: Machine Learning for...

In microbiome/mia: Microbiome analysis

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,
  assay.type = assay_name,
  assay_name = 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,
  assay.type = assay_name,
  assay_name = 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,
  assay.type = assay_name,
  assay_name = 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
assay.type	a single character value for specifying which assay to use for calculation.
assay_name	a single character value for specifying which assay to use for calculation. (Please use assay.type instead. At some point assay_name will be disabled.)
exprs_values	a single character value for specifying which assay to use for calculation. (Please use assay.type instead.)
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)

tse <- TreeSummarizedExperiment(assays = list(counts = counts),
                                colData = colData)

library(bluster)

# Compute DMM algorithm and store result in metadata
tse <- cluster(tse, name = "DMM", DmmParam(k = 1:3, type = "laplace"),
               MARGIN = "samples", full = TRUE)

# Get the list of DMN objects
metadata(tse)$DMM$dmm

# Get and display which objects fits best
bestFit <- metadata(tse)$DMM$best
bestFit

# Get the model that generated the best fit
bestModel <- metadata(tse)$DMM$dmm[[bestFit]]
bestModel

# Get the sample-cluster assignment probability matrix
head(metadata(tse)$DMM$prob)

# Get the weight of each component for the best model
bestModel@mixture$Weight

microbiome/mia documentation built on April 27, 2024, 4:04 a.m.