Statistical or biological significance?
Emma Saxon
- Correspondence:
Emma SaxonBMCBiologyEditorial@biomedcentral.com
BMC Biology, BioMed Central, 236 Gray’s Inn Road, London
WC1X 8HB, UK
BMC Biology 2015, 13:91 doi:10.1186/s12915-015-0198-1
The electronic version of this article is the complete one
and can be found online at:http://www.biomedcentral.com/1741-7007/13/91
Published:
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5 November 2015
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Oat plants grown at an agricultural research facility
produce higher yields in Field 1 than in Field 2, under well fertilised
conditions and with similar weather exposure; all oat plants in both fields are
healthy and show no sign of disease. In this study, the authors hypothesised
that the soil microbial community might be different in each field, and these
differences might explain the difference in oat plant growth. They carried out
a metagenomic analysis of the 16 s ribosomal ‘signature’ sequences from bacteria
in 50 randomly located soil samples in each field to determine the composition
of the bacterial community. The study identified >1000 species, most of
which were present in both fields. The authors identified two plant
growth-promoting species that were significantly reduced in soil from Field 2
(Student’s t-test P < 0.05), and concluded that these species
might have contributed to reduced yield.
The previous example in this series addressed the problem of
correcting for multiple comparisons. But even if the authors’ findings were
significant after applying a correction, there is still another issue: the
authors determined the levels of each bacterial species as a percentage of the
whole community, and not as their number per unit of soil, which is more
relevant to the potential biological effect of any difference between the
fields. Each sample sent for sequencing was taken from 1 g soil and
contained ~500,000 sequences, each assumed to correspond to one bacterial cell:
the two species where the difference between the two soils was significant
differ by only 0.0007 and 0.0008 %, corresponding to just 350–400 cells
[Fig. 1]. This
small number of bacterial cells is unlikely to have had a significant effect on
oat plant growth; although statistically significant, the results are not
likely to be biologically significant.
Fig. 1. The
proportion of nine known plant growth-promoting bacterial species detected in
the soil bacterial community of two fields. Oat plant yield was greater in
Field 1 than Field 2; two of the growth-promoting bacterial species were found
at a significantly lower level in Field 2 than Field 1 (Student’s t-test *P < 0.05;
error bars show standard deviation)
Another potential problem with this study is that although
the 16 s ribosomal sequence is commonly used to identify bacterial species
in metagenomic studies, many species have more than one copy of the 16 s
sequence in their genome. Studies of bacterial abundance, such as this one,
may, therefore, overestimate the number of bacterial species with a 16 s
copy number greater than one. In a 2012 study, Kembel and coworkers [1]
illustrated the importance of this problem by applying estimations of copy
number to previously published metagenomic data sets, based on known copy
numbers from diverse bacterial species. This adjustment for 16 s copy
number changed some of the original outcomes reported in the published studies:
in an oceanic data set, the ninth most abundant taxon became the second most
abundant, and in a human microbiome study, the bacterial community found in the
ear became more similar to that in the nostril rather than the sole of the foot
— a more intuitive result.
The authors of that study created software designed to
account for copy number, which can be used in conjunction with the open-source
software already used for analysing metagenomic data sets, such as QIIME
(Quantitative Insights Into Microbial Ecology) [1]. Correcting for
copy number can also be carried out using PICRUSt (Phylogenetic Investigation
of Communities by Reconstruction of Unobserved States) [2].
- Kembel
SW, Wu M, Eisen JA, Green JL. Incorporating 16S gene copy number
information improves estimates of microbial diversity and abundance. PLoS
Comput Biol. 2012; 8:e1002743. PubMed Abstract | Publisher Full Text
- Langille
MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA et al.. Predictive
functional profiling of microbial communities using 16S rRNA marker gene
sequences. Nat Biotechnol. 2013; 31:814-21. PubMed Abstract | Publisher Full Text
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