Author: Dirk Van den Poel, Michel Ballings, Andrey Volkov, Jeroen D'haen, Michiel Van Herwegen
Description: Convenience functions for data preparation and modeling often used in aCRM.

Title: Adaptive Set-Based Gene-Environment Interaction Test for Rare
Description: Tests gene-environment interaction for rare genetic variants within the framework of adaptive sum of powered score test. The package includes two tests: adaptive gene-by-environment interaction test, and joint test for genetic main effects and gene-environment interaction. See Yang et al (2018) <doi:10.1002/sim.8037>.
Authors@R: c(person("Tianzhong", "Yang", role = c("aut", "cre"),

Title: Testing for Compositional Pathologies in Datasets
Description: 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>).
Imports: matrixcalc, zCompositions, shiny, edgeR, ALDEx2, vegan

Title: aLFQ: An R-package for estimating absolute protein quantities
from label-free LC-MS/MS proteomics data
Author: George Rosenberger, Hannes Roest, Barry Grant
Depends: R (>= 2.15.0), data.table, plyr, caret, seqinr
Description: Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample.