Breakthrough method in microbial ecology: fMRI analysis of ordination plots.

As anyone who has read a recent article on microbial ecology knows, the name of the game is ordination plots. Looking for post-hoc patterns in 16S and metagenomics surveys is pretty much par for the course. Depending on your question and statistical inclinations there are a huge variety of ordination plots to choose from; NMDS, PCA, PCOA… not to mention the distance metrics; UniFrac (weighted or unweighted), Bray-Curtis, Jaccard, etc. One approach is to simply run all these analyses (QIIME does this by default) and then to look for patterns.

journal.pone_.0081330.g004-1024x367.png

journal.pone.0081330.g004
Figure from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0081330

But how to choose?   If one plot shows a pattern and another shows a different pattern, how is a researcher to decide? Statistics is one possible approach, but we all know what Mark Twain said about statistics.

Better than statistics, is a new approach described in this week’s issue of QIIME THERAPY. In “fMRI analysis of ordination plots”, the authors describe a breakthrough approach for rapid and accurate classification of ordination plots for microbial ecology. Instead of arbitrary statistics, the researchers harbor the power of the unconscious human mind.   Any human can look at a series of dots and see patterns but computers struggle with this simple task.   Ask a 5-year old what shapes they see in an ordination plot and they’ll have ideas.   But, as the authors describe, there are problems when scientists are involved… they tend to overthink the process and focus on irrelevant details like axis labels (meaningless in a PCA plot for example). In this work, the researchers circumvent the conscious brain by placing subjects in an fMRI machine and passing images of ordination plots through their field of vision. Patterns are classified on the basis of the strength of the reaction to the image. The authors show that this method is 93% accurate on mock community data and also unveiled patterns in tests data sets that had been missed by statisticians. In addition, this method takes 10% of the time and requires about 25% of the cost of a statistical analysis.

As a proof of principle, the authors applied this methodology to previously analyzed data from the Human Microbiome Project.   While generally supporting previous conclusions, the authors also found that weighted UniFrac analysis suggested a novel correlation between the microbiome of the Hallux (big toe) and the microbiome of the non-dominant hand of participants. They plan further research to understand the causality of this connection.

About Jonathan Eisen

I am an evolutionary biologist and a Professor at U. C. Davis. (see my lab site here). My research focuses on the origin of novelty (how new processes and functions originate). To study this I focus on sequencing and analyzing genomes of organisms, especially microbes and using phylogenomic analysis

5 thoughts on “Breakthrough method in microbial ecology: fMRI analysis of ordination plots.

  1. Your statement about QIIME is incorrect, if I’m understanding it. By default, QIIME’s core_diversity_analyses.py script (the most commonly used workflow for beta diversity analysis) computes weighted and unweighted UniFrac, and PCoA on each (it definitely does not run the ~30 supported beta diversity metrics and the two supported ordination methods for a total of 60 ordination plots). No functionality in QIIME computes all of these metrics by default.

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    1. Apologies for that. Indeed QIIME does not run all these by default. I should have said that many QIIME users run all these metrics and then look for patterns post-hoc.

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