testDAA: A function to test for differential absolute abundance on a...

In CenterForStatistics-UGent/reconsi: Resampling Collapsed Null Distributions for Simultaneous Inference

A function to test for differential absolute abundance on a phyloseq object

Description

A function to test for differential absolute abundance on a phyloseq object

Usage

testDAA(Y, ...)

## S4 method for signature 'phyloseq'
testDAA(Y, groupName, FCname, ...)

## S4 method for signature 'matrix'
testDAA(Y, FC, x, S = rowSums(Y), tieBreakRan = TRUE, assumeNormal = TRUE, ...)

Arguments

Y	A phyloseq object, or a data matrix with samples in the rows and OTUs in the columns
...	passed on to the reconsi() function
groupName	A character string, the name of a variable in physeq indicating the grouping factor
FCname	A character string, the name of a variable in physeq containing the total cell count
FC	a vector of length n with total flow cytometry cell counts
x	a grouping factor of length n
S	a vector of library sizes. Will be calculated if not provided
tieBreakRan	A boolean, should ties be broken at random.
assumeNormal	A boolean, should normality be assumed for the null distribution?

Value

See the reconsi() function

Examples

#Test for phyloseq object
library(phyloseq)
data("VandeputteData")
VandeputtePruned = prune_samples(Vandeputte,
samples = sample_names(Vandeputte)[20:40])
testVanDePutte = testDAA(VandeputtePruned, "Health.status", "absCountFrozen",
B = 15)
#Test for matrix
testMat = testDAA(as(otu_table(VandeputtePruned), "matrix"),
get_variable(VandeputtePruned, "Health.status"),
get_variable(VandeputtePruned,"absCountFrozen"), B = 15)

CenterForStatistics-UGent/reconsi documentation built on Nov. 13, 2023, 2:04 a.m.