ggloor/ALDEx_bioc: Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account
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.
Getting started
Browse package contents
Package details |
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| Author | Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng, Michelle Nixon |
| Bioconductor views | Bayesian ChIPSeq DNASeq DifferentialExpression GeneExpression ImmunoOncology Metagenomics Microbiome Posterior p-value RNASeq Scale simulation Sequencing Software Transcriptomics |
| Maintainer | Greg Gloor <ggloor@uwo.ca> |
| License | GPL (>=3) |
| Version | 1.35.0 |
| URL | https://github.com/ggloor/ALDEx_bioc |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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