Multiple comparisons
Emma Saxon
- Correspondence:
Emma SaxonBMCBiologyEditorial@biomedcentral.com
BMC Biology, BioMed Central, 236 Gray’s Inn Road, London
WC1X 8HB, UK
BMC Biology 2015, 13:86 doi:10.1186/s12915-015-0199-0
The electronic version of this article is the complete one
and can be found online at:http://www.biomedcentral.com/1741-7007/13/86
|
Published:
|
23 October 2015
|
© 2015 Saxon.
Open AccessThis article is distributed under the terms of
the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if changes were
made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made
available in this article, unless otherwise stated.
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.
High-throughput genomic studies produce large amounts of
data with the potential to be mined for information about gene regulation,
evolutionary relationships, the genetic components of disease and, in this
case, the composition of microbial communities in different environments.
However, researchers encounter several potential pitfalls when analysing ‘big
data’ that may lead to false conclusions. One of these is a problem of multiple
comparisons; in this example, the authors compared the levels of the >1000
bacterial species found in two different fields, many of which were found to
differ significantly between the two fields (Student’s t-test P < 0.05).
The researchers focussed on the nine known plant growth-promoting species that
were identified in their study (Fig. 1) in which
the levels of two species were found to be significantly decreased, leading to
the hypothesis that reduced plant growth-promoting species in Field 2 may have
contributed to reduced oat yields.
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)
However, significance at the level of P < 0.05
in a Student’s t-test means that the false positive rate —
incorrectly rejecting the null hypothesis that there is no difference between
the groups — is 5 %. When the number of comparisons is large, the
likelihood of false positive results is greater: for the 1000 comparisons in
this study, around 50 are expected falsely to show a significant increase or
decrease in level. The researchers in this case did not correct for multiple
comparisons, but several methods exist for doing so. The simplest of these is
the Bonferroni correction, which uses a P value calculated as
0.05/n, where n is the number of comparisons made. But this method can
‘over-correct’, leading to significant differences being overlooked; Nakagawa’s
2004 review provides some more detail on this subject [1]. More complex
methods, such as the Benjamini-Hochberg procedure, allow for a slightly less
strict correction, reducing the rate of false negative results that occur with
the Bonferroni method [2]. This is
particularly useful when the number of comparisons is very large, as is the
case in this study.
- Nakagawa
S. A farewell to Bonferroni: the problems of low statistical power
and publication bias. Behav Ecol. 2004; 15:1044-5. Publisher Full Text
- Benjamini
Y, Hochberg Y. Controlling the false discovery rate: a practical
and powerful approach to multiple testing. J Roy Stat Soc B
Methodol. 1995; 57:289-300.
Tomado de envio de bcm
No hay comentarios:
Publicar un comentario