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Volume 7, Issue 2 (Suppl)
J Ecosyst Ecography, an open access journal
ISSN:2157-7625
September 18-20, 2017
Page 26
conference
series
.com
September 18-20, 2017 Toronto, Canada
Joint Conference
International Conference on
International Conference on
Environmental Microbiology and Microbial Ecology
&
Ecology and Ecosystems
Cautionary tales from the microbiome: Finding what is real and reproducible
Statement of the Problem:
Commonly-used methods of analyzing microbiome or RNA-seq datasets can be misleading and all
the available information in a consistent manner are not in use. These results in many analyses being dominated by either the
most abundant, or the rarest features: In fact, it is often the case that the most abundant taxa dominate multivariate outputs,
and the rarest taxa dominate univariate outputs in the same dataset. Furthermore, these datasets have extraordinary properties
that make the use of correlation and network analysis problematic.
Methodology and Theoretical Orientation:
Data collected using high throughput sequencing (HTS) methods are sequence
reads mapped to genomic intervals, and are commonly analyzed as either normalized count data or relative abundance data.
One reason for these normalizations is to attempt to compensate for the problem that the sequencing instrument imposes an
upper bound on the number of sequence reads. Positive data with an arbitrary bound are compositional data and are subject to
the problem of spurious correlation. Thus, ordination, clustering and network analysis become unreliable. A second problem
is that the data are sparse: i.e., contain many 0 values. A third problem is that the largest measurement error is at the low count
margins in these datasets.
Conclusion & Significance:
We use microbiome datasets to show how Bayesian estimation combined with compositional data
approaches that examine the ratios between taxa give robust insights into the structure and function of microbial communities.
I will present example datasets drawn from the human and ecological domains and show that ordination, differential abundance
and correlation can be interpreted in an internally consistent manner that provides reproducible insights.
Biography
Greg Gloor is a professor of biochemistry with broad experience in molecular biology, genetics and genomics. Most recently, he has developed tools to investigate fun-
damentals of molecular evolution, microbial ecology and meta-transcriptomics. He is currently working on developing and adapting principled methods to characterize
correlation and differential abundance in sparse, high throughput sequencing data as generated in 16S rRNA gene sequencing surveys, meta-genomics and meta-tran-
scriptomics. He is the developer and maintainer of the ALDEx2 R package on Bioconductor.
ggloor@uwo.caGregory B Gloor
University of Western Ontario, Canada
Gregory B Gloor, J Ecosyst Ecography 2017, 7:2 (Suppl)
DOI: 10.4172/2157-7625-C1-028
Notes: