aldex.set.mode: identify set of denominator features for log-ratio...

In ggloor/ALDEx_bioc: Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account

identify set of denominator features for log-ratio calculation

Description

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

Usage

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

Arguments

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. Check that the same number of features are in the input and output datasets.

Details

Identify set of denominator features for log-ratio calculation

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

Value

Outputs a vector containing indices per condition, or a single vector in some cases.

Author(s)

Jia Rong Wu

References

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

See Also

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

Examples

# 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")

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