Tool: Software For Detecting Differential Abundance In Meta-Genomic Samples
10
gravatar for Istvan Albert
6.8 years ago by
Istvan Albert ♦♦ 81k
University Park, USA
Istvan Albert ♦♦ 81k wrote:

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

MetaStats

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

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

MetaRep

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.

LEfSe

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 • 5.7k views
ADD COMMENTlink modified 3.9 years ago by 5heikki8.5k • written 6.8 years ago by Istvan Albert ♦♦ 81k
1
gravatar for gregcaporaso
3.9 years ago by
gregcaporaso10
United States
gregcaporaso10 wrote:

Some approaches are now discussed in these papers:

Waste not, want not: why rarefying microbiome data is inadmissible. McMurdie and Holmes, 2014.
http://www.ncbi.nlm.nih.gov/pubmed/24699258

Analysis of composition of microbiomes: a novel method for studying microbial composition. Mandal et al., 2015.
http://www.ncbi.nlm.nih.gov/pubmed/26028277

 

 

 

 

ADD COMMENTlink written 3.9 years ago by gregcaporaso10
1
gravatar for 5heikki
3.9 years ago by
5heikki8.5k
Finland
5heikki8.5k 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 3.9 years ago • written 3.9 years ago by 5heikki8.5k
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