aldex.clr-class: The aldex.clr class
In ggloor/ALDEx_bioc: Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account

aldex.clr-class

R Documentation

The aldex.clr class

Description

The aldex.clr S4 class is a class which stores the data generated by the aldex.clr method.

Details

An aldex.clr object contains the centre-log ratio transformed Monte Carlo Dirichlet instances derived from estimating the technical variance of the raw read count data. It is created by the aldex.clr.function, which is invoked by the aldex.clr method. It consists of eight slots: the reads, the condition information, the number of instances, the denominator, whether it was invoked as verbose, and if multi-cores was used, the Dirichlet Monte-Carlo probabilities, and the centre-log ratio transformed Monte Carlo probabilities. These can be accessed along with information about the length of some attributes.

Value

The aldex.clr object contains the raw data, the estimated probabilities drawn from a Dirichlet distribution, and the clr transformed values for each Monte-Carlo instance. These can be accessed through getters outlined below.

Methods

In the code below, x is an aldex.clr object, and i, is a positive integer. There are N samples, D features, and M Monte-Carlo instances.

getMonteCarloInstances(x): Returns the clr transformed Monte Carlo Dirichlet instances as a list where each list entry is a single sample containing a D x M matrix.
getSampleIDs(x): Returns the names of the samples. These can be used to access the original reads for a given sample, as in x@reads\$sampleID (if the reads are a data frame).
getFeatureNames(x): Returns the names of the keys. These can be used to subset the data rows.
getFeatures(x): Returns the clr transformed values for the features in the first sample.
numFeatures(x): Returns the number of features that were non-0 in at least one sample.
numMCInstances(x): Returns the number of Monte-Carlo instances.
getReads(x): Returns the input data as used by the method.
numConditions(x): Returns the number of samples in the conditions analysis.
getMonteCarloReplicate(x, i): Returns the D x M matrix containing the Monte-Carlo instances for one sample.
getMonteCarloSample(x, i): Returns the N x D matrix containing Monte-Carlo instance i for for all samples.

Author(s)

Greg Gloor, Ruth Grace Wong, Andrew Fernandes, Jia Rong Wu and Matt Links contributed to this code

References

Please use the citation given by citation(package="ALDEx").

Examples


    # The 'reads' data.frame or
    # SummarizedExperiment object should have
    # row and column names that are unique,
    # and looks like the following:
    #
    #              T1a T1b  T2  T3  N1  N2  Nx
    #   Gene_00001   0   0   2   0   0   1   0
    #   Gene_00002  20   8  12   5  19  26  14
    #   Gene_00003   3   0   2   0   0   0   1
    #   Gene_00004  75  84 241 149 271 257 188
    #   Gene_00005  10  16   4   0   4  10  10
    #   Gene_00006 129 126 451 223 243 149 209
    #       ... many more rows ...

    data(selex)
    #subset for efficiency
    selex <- selex[1201:1600,]
    conds <- c(rep("NS", 7), rep("S", 7))
    i=1

    # x is an object of type aldex.clr
    x <- aldex.clr(selex, conds, mc.samples = 2, denom="all", verbose = FALSE)

    # get reads plus uniform prior
    reads <- getReads(x)

    # get a list containing all of the clr transformed instances
    monteCarloInstances <- getMonteCarloInstances(x)

    # get a list containing all of the Monte-Carlo Dirichlet instances
    monteCarloDirInstances <- getDirichletInstances(x)

    # retrieve the clr transformed instances for sample i.
    monteCarloInstance <- getMonteCarloReplicate(x,i)

    # retrieve the Monte-Carlo Dirichlet instances for sample i.
    monteCarloDirInstance <- getDirichletReplicate(x,i)

    # retrieve the clr transformed instance i for all samples
    monteCarloSample <- getMonteCarloSample(x,i)

    # retrieve the Monte-Carlo Dirichlet instance i for all samples
    monteCarloDirSample <- getDirichletSample(x,i)

    # get sample names
    sampleIDs <- getSampleIDs(x)

    # get features
    features <- getFeatures(x)

    # get number of features with at least one count
    numFeatures <- numFeatures(x)

    # get number of Monte Carlo instances
    numInstances <- numMCInstances(x)

    # get names of features
    featureNames <- getFeatureNames(x)

    # get number of conditions
    conditions <- numConditions(x)

    # get the offset of the features in the log-ratio  denominator
    denom <- getDenom(x)

ggloor/ALDEx_bioc documentation built on Oct. 31, 2023, 1:13 a.m.