8.7 Other Resources

Elizabeth Bik keeps an excellent Microbiome Papers Collection of a few dozen classic academic papers.

and you’ll find even more in Tyler, Smith, and Silverberg (2014), which is strongly recommended.

8.7.1 Software

ANCOM (Mandal et al. (2015)) is an open source software tool142 to help understand abundances.

When we compare populations from one ecosystem (e.g. my results on Monday) with another (e.g. my results on Tuesday), there is a fundamental statistical sense in which the two populations are not comparable.

This paper gives the analogy of trying to compare two forests after capturing 100 animals in each: you count 20 bears in one and 30 in the other. There are statistical ways to say with confidence that the first forest is composed of 20% bears and the other 30%, but there is no way to conclude that the second forest has more bears without knowing the total number of animals in each.

A reliance on relative abundances (i.e. percentages) carries other, statistical, problems. For example, the Pearson correlation coefficient is difficult to interpret, since the sum-to-one characteristic of relative abundances requires mathematically that there be some negative correlations. If the numbers were absolute, you wouldn’t necessarily have negative correlations.


Mandal, Siddhartha, Will Van Treuren, Richard A. White, Merete Eggesbø, Rob Knight, and Shyamal D. Peddada. 2015. “Analysis of Composition of Microbiomes: A Novel Method for Studying Microbial Composition.” Microbial Ecology in Health & Disease 26 (0). https://doi.org/10.3402/mehd.v26.27663.
Tyler, Andrea D, Michelle I Smith, and Mark S Silverberg. 2014. “Analyzing the Human Microbiome: A How To Guide for Physicians.” The American Journal of Gastroenterology 109 (7): 983–93. https://doi.org/10.1038/ajg.2014.73.