ALDEx2 (version 1.4.0)

aldex.clr: Compute an aldex.clr Object

Description

Generate Monte Carlo samples of the Dirichlet distribution for each sample. Convert each instance using the centred log-ratio transform This is the input for all further analyses.

Usage

aldex.clr(reads, mc.samples = 128, verbose = FALSE, useMC=FALSE)

Arguments

reads

a data.frame or RangedSummarizedExperiment object containing non-negative integers only and with unique names for all rows and columns, where each row is a different gene and each column represents a sequencing read-count. Rows with 0 reads in each sample are deleted prior to analysis

mc.samples

the number of Monte Carlo samples to use to estimate the underlying distributions; since we are estimating central tendencies, 128 is usually sufficient

verbose

Print diagnostic information while running. Useful only for debugging if fails on large datasets

useMC

use multicore by default (FALSE). Multi core processing will be attempted with the BiocParallel package, then the parallel package. If neither are installed, serial processing will be used.

Value

The object produced by the clr function contains the clr transformed values for each Monte-Carlo Dirichlet instance, which can be accessed through getMonteCarloInstances(x), where x is the clr function output. Each list element is named by the sample ID. getFeatures(x) returns the features, getSampleIDs(x) returns sample IDs, and getFeatureNames(x) returns the feature names.

Details

An explicit description of the input format for the reads object is shown under `Examples', below.

References

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

See Also

aldex.ttest, aldex.glm, aldex.effect, selex

Examples

    # The 'reads' data.frame or
    # RangedSummarizedExperiment 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)
    x <- aldex.clr(selex, mc.samples = 2, verbose = FALSE)