Testing for Compositional Pathologies in Datasets
Calculate the subcompositional coherence of samples in a dataset for a...
aIc.dominant calculates the subcompositional dominance of a sample i...
aIc.perturb calculates the perturbation invariance of distance for s...
aIc.plot plots the result of the distance tests.
aIc.runExample loads the associated shiny app This will load the sel...
aIc.scale calculates the scaling invariance of a sample in a dataset...
aIc.singular tests for singular data. This is expected to be true if...
A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) 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) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).