aldex.clr.function-methods: Compute an 'aldex.clr' Object
In ALDEx2: Analysis Of Differential Abundance Taking Sample Variation Into Account

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1	aldex.clr(reads, conds, mc.samples = 128, denom="all", verbose=FALSE, useMC=FALSE)

`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.
`conds`	A `vector` containing a descriptor for the samples, allowing them to be grouped and compared.
`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.
`denom`	An `any` variable (all, iqlr, zero, lvha, median,user) indicating features to use as the denominator for the Geometric Mean calculation The default "all" uses the geometric mean abundance of all features. Using "median" returns the median abundance of all features. Using "iqlr" uses the features that are between the first and third quartile of the variance of the clr values across all samples. Using "zero" uses the non-zero features in each grop as the denominator. This approach is an extreme case where there are many nonzero features in one condition but many zeros in another. Using "lvha" uses features that have low variance (bottom quartile) and high relative abundance (top quartile in every sample). It is also possible to supply a vector of row indices to use as the denominator. Here, the experimentalist is determining a-priori which rows are thought to be invariant. In the case of RNA-seq, this could include ribosomal protein genes and and other house-keeping genes.
`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. Serial processing will be used if this is not possible.

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

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.

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

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

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

    # 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)
    #subset for efficiency
    selex <- selex[1201:1600,]
    conds <- c(rep("NS", 7), rep("S", 7))
    x <- aldex.clr(selex, conds, mc.samples=2, denom="all", verbose=FALSE)