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aIc (version 1.0)

Testing for Compositional Pathologies in Datasets

Description

A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) ), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) ) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages 'ALDEx2', 'edgeR' and 'DESeq2' (Fernandes et al (2014) , Anders et al. (2013)).

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install.packages('aIc')

Monthly Downloads

238

Version

1.0

License

GPL (>= 3)

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Repository

https://github.com/ggloor/aIc

Maintainer

Greg Gloor

Last Published

October 4th, 2022

Functions in aIc (1.0)

aIc.plot plots the result of the distance tests.

meta-transcriptome data

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.

Selection-based differential sequence variant abundance dataset

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.

single cell transcriptome data

Saccharomyces cerevisiae transcriptome

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.

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.

16S rRNA tag-sequencing data

Calculate the subcompositional coherence of samples in a dataset for a given correction.

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.