A collection of tools that detect differential abundances in metagenomic samples. (Please add new related tools as answers)
- Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples (PloS Comp. Bio, 2009)
Presents a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. The method employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test.
- MetaPath: identifying differentially abundant metabolic pathways in metagenomic datasets (BMC Proceedings, 2011)
Identifies differentially abundant pathways in metagenomic datasets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge
- METAREP: JCVI metagenomics reports—an open source tool for high-performance comparative metagenomics (Bioinformatics, 2010)
Allows users to compare absolute and relative counts of multiple datasets at various functional and taxonomic levels. Advanced comparative features comprise statistical tests as well as multidimensional scaling, heatmap and hierarchical clustering plots.
It is an algorithm for high-dimensional biomarker discovery and explanation that identifies genomic features (genes, pathways, or taxa) characterizing the differences between two or more biological conditions (or classes, see figure below). It emphasizes both statistical significance and biological relevance, allowing researchers to identify differentially abundant features that are also consistent with biologically meaningful categories (subclasses). LEfSe first robustly identifies features that are statistically different among biological classes. It then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior.