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Microbiome Analysis pp 193–213Cite as

From RNA-seq to Biological Inference: Using Compositional Data Analysis in Meta-Transcriptomics

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1849))

Abstract

The proper analysis of high-throughput sequencing datasets of mixed microbial communities (meta-transcriptomics) is substantially more complex than for datasets composed of single organisms. Adapting commonly used RNA-seq methods to the analysis of meta-transcriptome datasets can be misleading and not use all the available information in a consistent manner. However, meta-transcriptomic experiments can be investigated in a principled manner using Bayesian probabilistic modeling of the data at a functional level coupled with analysis under a compositional data analysis paradigm. We present a worked example for the differential functional evaluation of mixed-species microbial communities obtained from human clinical samples that were sequenced on an Illumina platform. We demonstrate methods to functionally map reads directly, conduct a compositionally appropriate exploratory data analysis, evaluate differential relative abundance, and finally identify compositionally associated (constant ratio) functions. Using these approaches we have found that meta-transcriptomic functional analyses are highly reproducible and convey significant information regarding the ecosystem.

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Authors and Affiliations

  1. Department of Biochemistry, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada

    Jean M. Macklaim & Gregory B. Gloor

  2. Canadian Centre for Human Microbiome and Probiotic Research, Lawson Health Sciences Centre, London, ON, Canada

    Jean M. Macklaim & Gregory B. Gloor

Authors
  1. Jean M. Macklaim
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  2. Gregory B. Gloor
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Corresponding author

Correspondence to Gregory B. Gloor .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada

    Robert G. Beiko

  2. Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada

    Will Hsiao

  3. Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, Canada

    John Parkinson

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Macklaim, J.M., Gloor, G.B. (2018). From RNA-seq to Biological Inference: Using Compositional Data Analysis in Meta-Transcriptomics. In: Beiko, R., Hsiao, W., Parkinson, J. (eds) Microbiome Analysis. Methods in Molecular Biology, vol 1849. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8728-3_13

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  • DOI: https://doi.org/10.1007/978-1-4939-8728-3_13

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8726-9

  • Online ISBN: 978-1-4939-8728-3

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