aldex.set.mode: identify set of denominator features for log-ratio...
In ALDEx2: Analysis Of Differential Abundance Taking Sample Variation Into Account

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

calculate the features that are to be used as the denominator for the Geometric Mean calculation in clr_function.R

1	aldex.set.mode(reads, conds, denom="all")

`reads`	A data frame containing the samples and features per sample.
`conds`	A vector describing which samples belong to what condition.
`denom`	Character argument specifying which indicies to return. 'all' returns all features in both conditons. 'zero' returns the nonzero count features per condition. 'iqlr' returns the features whose variance falls within the inter-quantile range of the CLR-transformed data. In cases of malformed or null queries, input defaults to 'all'. Additionally, the input can be a numeric vector, which contains a set of row indicies to center the data against. Only for advanced users who can pre-determine the invariant set of features within their data.

An explicit example for two conditions is shown in the ‘Examples’ below.

Outputs a vector containing indicies per condition.

Jia Rong Wu

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

aldex.clr, aldex.ttest, aldex.effect, selex

    # x is the output of the \code{x <- clr(data, mc.samples)} function
    # conditions is a description of the data
    # for the selex dataset, conditions <- c(rep("N", 7), rep("S", 7))
    # input can be "all", "iqlr", "zero" or numeric for advanced users
    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")