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aIc: Testing for Compositional Pathologies in Datasets

A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) &lt;<a href="https://doi.org/10.1016%2Fj.acags.2020.100026" target="_top">doi:10.1016/j.acags.2020.100026</a>&gt;), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) &lt;<a href="https://doi.org/10.1007%2FBF00891269" target="_top">doi:10.1007/BF00891269</a>&gt;) 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) &lt;<a href="https://doi.org/10.1186%2F2049-2618-2-15" target="_top">doi:10.1186/2049-2618-2-15</a>&gt;, Anders et al. (2013)&lt;<a href="https://doi.org/10.1038%2Fnprot.2013.099" target="_top">doi:10.1038/nprot.2013.099</a>&gt;).



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aIc: Testing for Compositional Pathologies in Datasets

https://cran.rstudio.com/web/packages/aIc/index.html

A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) &lt;<a href="https://doi.org/10.1016%2Fj.acags.2020.100026" target="_top">doi:10.1016/j.acags.2020.100026</a>&gt;), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) &lt;<a href="https://doi.org/10.1007%2FBF00891269" target="_top">doi:10.1007/BF00891269</a>&gt;) 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) &lt;<a href="https://doi.org/10.1186%2F2049-2618-2-15" target="_top">doi:10.1186/2049-2618-2-15</a>&gt;, Anders et al. (2013)&lt;<a href="https://doi.org/10.1038%2Fnprot.2013.099" target="_top">doi:10.1038/nprot.2013.099</a>&gt;).



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https://cran.rstudio.com/web/packages/aIc/index.html

aIc: Testing for Compositional Pathologies in Datasets

A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) &lt;<a href="https://doi.org/10.1016%2Fj.acags.2020.100026" target="_top">doi:10.1016/j.acags.2020.100026</a>&gt;), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) &lt;<a href="https://doi.org/10.1007%2FBF00891269" target="_top">doi:10.1007/BF00891269</a>&gt;) 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) &lt;<a href="https://doi.org/10.1186%2F2049-2618-2-15" target="_top">doi:10.1186/2049-2618-2-15</a>&gt;, Anders et al. (2013)&lt;<a href="https://doi.org/10.1038%2Fnprot.2013.099" target="_top">doi:10.1038/nprot.2013.099</a>&gt;).

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