Compositional approaches are beginning to permeate high throughput biomedical sciences in the areas of microbiome, genomics, transcriptomics and proteomics.Yet non-compositional approaches STREST are still commonly observed.Non-compositional approaches are particularly problematic in network analysis based on correlation, ordination and exploratory data analysis based on distance, and differential abundance analysis based on normalization.Here we describe the aIc R package, a simple tool that answers the fundamental question: does the dataset or normalization exhibit compositional artefacts that will skew interpretations when analyzing high throughput biomedical data? The aIc R package includes options for several of the most widely used normalizations and filtering methods.
The R package includes tests for subcompositional dominance and coherence along with perturbation and Incontinence Pads scale invariance.Exploratory analysis is facilitated by an R Shiny app that makes the process simple for those not wishing to use an R console.This simple approach will allow research groups to acknowledge and account for potential artefacts in data analysis resulting in more robust and reliable inferences.