Testing for Compositional Pathologies in Datasets

---

Documentation for package ‘aIc’ version 1.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

aIc.coherent	Calculate the subcompositional coherence of samples in a dataset for a given correction.
aIc.dominant	'aIc.dominant' calculates the subcompositional dominance of a sample in a dataset for a given correction. This compares the distances of samples of the full dataset and a subset of the dataset. This is expected to be true if the transform is behaving rationally in compositional datasets.
aIc.perturb	'aIc.perturb' calculates the perturbation invariance of distance for samples with a given correction. This compares the distances of samples of the full dataset and a the perturbed dataset. This is expected to be true if the transform is behaving rationally in compositional datasets.
aIc.plot	'aIc.plot' plots the result of the distance tests.
aIc.runExample	'aIc.runExample' loads the associated shiny app This will load the selex example dataset with the default group sizes, the user can upload their own local dataset and adjust groups accordingly.
aIc.scale	'aIc.scale' calculates the scaling invariance of a sample in a dataset for a given correction. This compares the distances of samples of the full dataset and a scaled version of the dataset. This is expected to be true if the transform is behaving rationally in compositional datasets.
aIc.singular	'aIc.singular' tests for singular data. This is expected to be true if the transform is behaving rationally in compositional datasets and also true in the case of datasets with more features than samples.
meta16S	16S rRNA tag-sequencing data
metaTscome	meta-transcriptome data
selex	Selection-based differential sequence variant abundance dataset
singleCell	single cell transcriptome data
transcriptome	Saccharomyces cerevisiae transcriptome