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_1.0.tgz(r-4.6-any)aIc_1.0.tgz(r-4.5-any)
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aIc_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
aIc/json (API)

# Install 'aIc' in R:

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

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:

4.08 score 2 stars 12 scripts 425 downloads 7 exports 86 dependencies

Last updated from:ada8f7e396. Checks:7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	WARNING	229
source / vignettes	OK	272
linux-release	WARNING	285
macos-release	WARNING	290
macos-oldrel	WARNING	296
windows-devel	WARNING	260
windows-release	WARNING	227
windows-oldrel	WARNING	239
wasm-release	OK	146

Exports:aIc.coherent aIc.dominant aIc.perturb aIc.plot aIc.runExample aIc.scale aIc.singular

Dependencies:abind ALDEx2 base64enc BH Biobase BiocGenerics BiocParallel bslib cachem cli cluster codetools commonmark cpp11 data.table DelayedArray deldir digest directlabels edgeR fastmap fontawesome formatR fs futile.logger futile.options generics GenomicRanges glue htmltools httpuv interp IRanges jpeg jquerylib jsonlite lambda.r later lattice latticeExtra lifecycle limma locfit magrittr MASS Matrix matrixcalc MatrixGenerics matrixStats memoise mgcv mime multtest NADA nlme otel permute png promises R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen RcppParallel Rfast rlang S4Arrays S4Vectors sass Seqinfo shiny snow sourcetools SparseArray statmod SummarizedExperiment survival truncnorm vegan withr xtable XVector zCompositions zigg

aIc: am I compositional?

aIc and amIcomp implement simple test functions to determine if a particular dataset and transformation exhibit data pathologies that we can understand and control for. Since 'all models are wrong'[Box:1976] it is important to know at the outset what is wrong, why it is wrong, and how that wrongness would hinder general interpretability in some way. | List of common data pathologies: | Properties of datasets | Example functions | Sub-compositions and correlation (coherence) | Sub-compositions and distance (dominance) | Testing for scale invariance | Testing for perturbation invariance | Choosing parameters | Conclusion | SessionInfo

Rendered fromaIc_vignette.Rmdusingknitr::rmarkdown

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

Help page	Topics
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 data	meta16S
meta-transcriptome data	metaTscome
Selection-based differential sequence variant abundance dataset	selex
single cell transcriptome data	singleCell
Saccharomyces cerevisiae transcriptome	transcriptome

Package: aIc 1.0

aIc: Testing for Compositional Pathologies in Datasets

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