Package: aIc 1.0

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) <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>).

Authors:Greg Gloor

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aIc.pdf |aIc.html✨
aIc/json (API)

# Install 'aIc' in R:
install.packages('aIc', repos = c('https://ggloor.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/ggloor/aic/issues

Datasets:
  • meta16S - 16S rRNA tag-sequencing data
  • metaTscome - Meta-transcriptome data
  • selex - Selection-based differential sequence variant abundance dataset
  • singleCell - Single cell transcriptome data
  • transcriptome - Saccharomyces cerevisiae transcriptome

On CRAN:

7 exports 2 stars 0.95 score 94 dependencies 11 scripts 406 downloads

Last updated 8 months agofrom:ada8f7e396. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 14 2024
R-4.5-winWARNINGSep 14 2024
R-4.5-linuxWARNINGSep 14 2024
R-4.4-winWARNINGSep 14 2024
R-4.4-macWARNINGSep 14 2024
R-4.3-winWARNINGSep 14 2024
R-4.3-macWARNINGSep 14 2024

Exports:aIc.coherentaIc.dominantaIc.perturbaIc.plotaIc.runExampleaIc.scaleaIc.singular

Dependencies:abindALDEx2askpassbase64encBHBiobaseBiocGenericsBiocParallelbslibcachemcliclustercodetoolscommonmarkcpp11crayoncurlDelayedArraydeldirdigestdirectlabelsedgeRfastmapfontawesomeformatRfsfutile.loggerfutile.optionsGenomeInfoDbGenomeInfoDbDataGenomicRangesgluehtmltoolshttpuvhttrinterpIRangesjpegjquerylibjsonlitelambda.rlaterlatticelatticeExtralifecyclelimmalocfitmagrittrMASSMatrixmatrixcalcMatrixGenericsmatrixStatsmemoisemgcvmimemulttestNADAnlmeopensslpermutepngpromisesquadprogR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppZigguratRfastrlangS4ArraysS4VectorssassshinysnowsourcetoolsSparseArraystatmodSummarizedExperimentsurvivalsystruncnormUCSC.utilsveganwithrxtableXVectorzCompositionszlibbioc

aIc: am I compositional?

Greg Gloor

Rendered fromaIc_vignette.Rmdusingknitr::rmarkdownon Sep 14 2024.

Last update: 2024-01-18
Started: 2022-06-24

Readme and manuals

Help Manual

Help pageTopics
Calculate the subcompositional coherence of samples in a dataset for a given correction.aIc.coherent
'aIc.dominant' calculates the subcompositional dominance of a sample in a dataset for a given correction. This compares the distances of samples of the full dataset and a subset of the dataset. This is expected to be true if the transform is behaving rationally in compositional datasets.aIc.dominant
'aIc.perturb' calculates the perturbation invariance of distance for samples with a given correction. This compares the distances of samples of the full dataset and a the perturbed dataset. This is expected to be true if the transform is behaving rationally in compositional datasets.aIc.perturb
'aIc.plot' plots the result of the distance tests.aIc.plot
'aIc.runExample' loads the associated shiny app This will load the selex example dataset with the default group sizes, the user can upload their own local dataset and adjust groups accordingly.aIc.runExample
'aIc.scale' calculates the scaling invariance of a sample in a dataset for a given correction. This compares the distances of samples of the full dataset and a scaled version of the dataset. This is expected to be true if the transform is behaving rationally in compositional datasets.aIc.scale
'aIc.singular' tests for singular data. This is expected to be true if the transform is behaving rationally in compositional datasets and also true in the case of datasets with more features than samples.aIc.singular
16S rRNA tag-sequencing datameta16S
meta-transcriptome datametaTscome
Selection-based differential sequence variant abundance datasetselex
single cell transcriptome datasingleCell
Saccharomyces cerevisiae transcriptometranscriptome