ALDEx2 (version 1.4.0)

aldex: Compute an aldex Object

Description

Generate Monte Carlo samples of the Dirichlet distribution for each sample. Convert each instance using the centred log-ratio transform Return two sample test values (Welch's t, Wilcoxon) or multi-sample test values (glm or Kruskal Wallace). Returns effect size values by defaulte.

Usage

aldex(reads, conditions, mc.samples=128, test="t", effect=TRUE, include.sample.summary=FALSE, verbose=FALSE)

Arguments

reads

a non-negative, integer-only containing data.frame that has 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

conditions

a description of the data structure to be used for testing

test

which tests to perform: t = Welch's t and Wilcoxon, glm = Kruskal Wallace and glm

effect

calculate abundances and effect sizes

include.sample.summary

include median clr values for each sample, defaults to FALSE

verbose

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

Value

return values of aldex.ttest, aldex.glm, and aldex.effect for explanations and example

Details

An explicit description of the input format for the reads object is shown under `Examples', below. This is not intended to be the generic function. The system is intended to be used for demonstration or instructional purposes.

References

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

See Also

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

Examples

    # The 'reads' data.frame 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)
    conds <- c(rep("N", 7), rep("S",7))
    x <- aldex(selex, conds, mc.samples = 2, test="t", effect=FALSE, verbose = FALSE)