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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) <<a href="https://doi.org/10.1016%2Fj.acags.2020.100026" target="_top">doi:10.1016/j.acags.2020.100026</a>>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <<a href="https://doi.org/10.1007%2FBF00891269" target="_top">doi:10.1007/BF00891269</a>>) 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) <<a href="https://doi.org/10.1186%2F2049-2618-2-15" target="_top">doi:10.1186/2049-2618-2-15</a>>, Anders et al. (2013)<<a href="https://doi.org/10.1038%2Fnprot.2013.099" target="_top">doi:10.1038/nprot.2013.099</a>>).
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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) <<a href="https://doi.org/10.1016%2Fj.acags.2020.100026" target="_top">doi:10.1016/j.acags.2020.100026</a>>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <<a href="https://doi.org/10.1007%2FBF00891269" target="_top">doi:10.1007/BF00891269</a>>) 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) <<a href="https://doi.org/10.1186%2F2049-2618-2-15" target="_top">doi:10.1186/2049-2618-2-15</a>>, Anders et al. (2013)<<a href="https://doi.org/10.1038%2Fnprot.2013.099" target="_top">doi:10.1038/nprot.2013.099</a>>).
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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) <<a href="https://doi.org/10.1016%2Fj.acags.2020.100026" target="_top">doi:10.1016/j.acags.2020.100026</a>>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <<a href="https://doi.org/10.1007%2FBF00891269" target="_top">doi:10.1007/BF00891269</a>>) 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) <<a href="https://doi.org/10.1186%2F2049-2618-2-15" target="_top">doi:10.1186/2049-2618-2-15</a>>, Anders et al. (2013)<<a href="https://doi.org/10.1038%2Fnprot.2013.099" target="_top">doi:10.1038/nprot.2013.099</a>>).
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5- titleCRAN: Package aIc
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5- og:titleaIc: Testing for Compositional Pathologies in Datasets
- og:descriptionA set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <<a href="https://doi.org/10.1016%2Fj.acags.2020.100026" target="_top">doi:10.1016/j.acags.2020.100026</a>>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <<a href="https://doi.org/10.1007%2FBF00891269" target="_top">doi:10.1007/BF00891269</a>>) 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) <<a href="https://doi.org/10.1186%2F2049-2618-2-15" target="_top">doi:10.1186/2049-2618-2-15</a>>, Anders et al. (2013)<<a href="https://doi.org/10.1038%2Fnprot.2013.099" target="_top">doi:10.1038/nprot.2013.099</a>>).
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11- https://CRAN.R-project.org/package=aIc
- https://doi.org/10.1007%2FBF00891269
- https://doi.org/10.1016%2Fj.acags.2020.100026
- https://doi.org/10.1038%2Fnprot.2013.099
- https://doi.org/10.1186%2F2049-2618-2-15