aldex.glm.effect: calculate effect sizes and differences between all contrasts...

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

calculate effect sizes and differences between all contrasts for the aldex.glm model matrix

Description

data for this function is saved in a list with entries named by contrast determines the median clr abundance of the feature in all samples and in groups determines the median difference between the two groups determines the median variation within each two group determines the effect size, which is the median of the ratio of the between group difference and the larger of the variance within groups

Usage

aldex.glm.effect(clr, verbose = TRUE, include.sample.summary = FALSE,
  useMC=FALSE, CI=FALSE)

Arguments

clr	The data output of aldex.clr
verbose	Print diagnostic information while running. Useful only for debugging if fails on large datasets
include.sample.summary	include median clr values for each sample, defaults to FALSE
useMC	use multicore by default (FALSE)
CI	give effect 95% confidence intervals, defaults to FALSE

Details

Calculate effect sizes and differences between all contrasts for the aldex.glm model matrix

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

Value

A dataframe with the following information:

rab.all

a vector containing the median clr value for each feature

rab.win.conditionA

a vector containing the median clr value for each feature in condition A

rab.win.conditionB

a vector containing the median clr value for each feature in condition B

diff.btw

a vector containing the per-feature median difference between condition A and B

diff.win

a vector containing the per-feature maximum median difference between Dirichlet instances within conditions

effect

a vector containing the per-feature effect size

overlap

a vector containing the per-feature proportion of effect size that is 0 or less

Author(s)

Greg Gloor, Andrew Fernandes, Matt Links

References

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

See Also

aldex.clr, aldex.effect, aldex.ttest, aldex.glm, 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))
data(selex)
#subset for efficiency
selex <- selex[1201:1600,]
covariates <- data.frame("A" = sample(0:1, 14, replace = TRUE),
"B" = c(rep(0, 7), rep(1, 7)),
"Z" = sample(c(1,2,3), 14, replace=TRUE))
mm <- model.matrix(~ A + Z + B, covariates)
x <- aldex.clr(selex, mm, mc.samples=8, denom="all")
glm.effect <- aldex.glm.effect(x)

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