Compositional uncertainty should not be ignored in high-throughput sequencing data analysis


  • Gregory Brian Gloor The University of Western Ontario
  • Jean M. Macklaim Department of Biochemistry The University of Western Ontario
  • Michael Vu Department of Biochemistry The University of Western Ontario
  • Andrew D. Fernandes Department of Applied Mathematics London, Canada



High throughput sequencing generates sparse compositional data, yet these datasets are rarely analyzed using a compositional approach. In addition, the variation inherent in these datasets is rarely acknowledged, but ignoring it can result in many false positive inferences. We demonstrate that examination of point estimates of the data can result in false positive results, even with appropriate zero replacement approaches, using an in vitro selection dataset with an outside standard of truth. The variation inherent in real high-throughput sequencing datasets is demonstrated, and we show that this varia- tion can be approximated, and hence accounted for, by Monte-Carlo sampling from the Dirichlet distribution. This approximation when used by itself is itself problematic, but becomes useful when coupled with a log-ratio approach commonly used in compositional data analysis. Thus, the approach illustrated here that merges Bayesian estimation with principles of compositional data analysis should be generally useful for high-dimensional count compositional data of the type generated by high throughput sequencing. 

Author Biography

Gregory Brian Gloor, The University of Western Ontario

Professor, Department of Biochemistry


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How to Cite

Gloor, G. B., Macklaim, J. M., Vu, M., & Fernandes, A. D. (2016). Compositional uncertainty should not be ignored in high-throughput sequencing data analysis. Austrian Journal of Statistics, 45(4), 73–87.



Compositional Data Analysis