Tool: Software For Detecting Differential Abundance In Meta-Genomic Samples
gravatar for Istvan Albert
7.9 years ago by
Istvan Albert ♦♦ 84k
University Park, USA
Istvan Albert ♦♦ 84k wrote:

A collection of tools that detect differential abundances in metagenomic samples. (Please add new related tools as answers)


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.


Identifies differentially abundant pathways in metagenomic datasets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge


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.

metagenomics tool • 6.3k views
ADD COMMENTlink modified 5.0 years ago by 5heikki9.0k • written 7.9 years ago by Istvan Albert ♦♦ 84k
gravatar for gregcaporaso
5.0 years ago by
United States
gregcaporaso20 wrote:

Some approaches are now discussed in these papers:

Waste not, want not: why rarefying microbiome data is inadmissible. McMurdie and Holmes, 2014.

Analysis of composition of microbiomes: a novel method for studying microbial composition. Mandal et al., 2015.

ADD COMMENTlink modified 10 months ago by RamRS30k • written 5.0 years ago by gregcaporaso20
gravatar for 5heikki
5.0 years ago by
5heikki9.0k wrote:

I've generated rarefaction data from e.g. kegg pathway annotations utilizing shuf, sort, uniq, awk and grep. It's very simple stuff, but can reveal interesting differences between samples, e.g. below I can immediately see that one sample is more rich in the context of functions.

I suppose HUMAnN is also relevant to the discussion. It's such a shame (but understandable) that they're ditching KEGG for MetaCyc in version 2.

ADD COMMENTlink modified 5.0 years ago • written 5.0 years ago by 5heikki9.0k
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