| Autor: |
Gregory Brian Gloor |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Austrian Journal of Statistics, Vol 52, Iss 4 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1026-597X |
| DOI: |
10.17713/ajs.v52i4.1617 |
| Popis: |
Compositional approaches are beginning to permeate high throughput biomedical sciences in the areas of microbiome, genomics, transcriptomics and proteomics. Yet non-compositional approaches 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 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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