Analysis Of Differential Abundance Taking Sample and Scale
Variation Into Account
Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links,
    Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng, Michelle Nixon
DifferentialExpression, RNASeq, Transcriptomics,
GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing,
Software, Microbiome, Metagenomics, ImmunoOncology, Scale
simulation, Posterior p-value
A differential abundance analysis for the comparison of two or more
    conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays
    as well as selected and unselected values from in-vitro sequence selections.
    Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for
    three or more experimental replicates. The method infers biological and sampling
    variation to calculate the expected false discovery rate, given the variation, based
    on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test
    (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test
    (via aldex.corr). All tests report predicted p-values and posterior
    Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized
    effect sizes for paired or unpaired study designs. ALDEx2 can now be used to estimate 
    the effect of scale on the results and report on the scale-dependent robustness of 
    results.
methods, stats, zCompositions, lattice, latticeExtra
Rfast, BiocParallel, GenomicRanges, IRanges, S4Vectors,
SummarizedExperiment, multtest, directlabels
testthat, BiocStyle, knitr, rmarkdown, purrr, ggpattern,
ggplot2, cowplot, tidyverse, magick