ALDEx2

ALDEx2 is a compositional data analysis tool designed to enhance the statistical analysis of high-throughput sequencing datasets, including RNA-seq, ChIP-seq, 16S rRNA gene sequencing, metagenomic analysis, and selective growth experiments. Despite the fundamental similarities in data structure across these various experimental designs—namely, counts of sequencing reads mapped to numerous features—traditional data analysis methods have remained disparate and non-transferable between experiment types.

ALDEx2 addresses this challenge by employing compositional data analysis methods from the physical and geological sciences, which convert raw data into relative abundances. This transformation leads to analyses that are more robust and reproducible. Utilizing Bayesian methods to infer technical and statistical errors, ALDEx2 has demonstrated its applicability and effectiveness across diverse datasets. It accurately identifies differential abundance and the direction of changes in selective growth experiments, aligns closely with leading tools in identifying differentially expressed genes in RNA-seq datasets, and successfully distinguishes differential taxa in the Human Microbiome Project 16S rRNA gene abundance dataset.