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SigTree: A Microbial Community Analysis Tool to Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree



Microbial community analysis experiments to assess the effect of a treatment intervention (or environmental change) on the relative abundance levels of multiple related microbial species (or operational taxonomic units) simultaneously using high throughput genomics are becoming increasingly common. Within the framework of the evolutionary phylogeny of all species considered in the experiment, this translates to a statistical need to identify the phylogenetic branches that exhibit a significant consensus response (in terms of operational taxonomic unit abundance) to the intervention. We present the R software package SigTree, a collection of flexible tools that make use of meta-analysis methods and regular expressions to identify and visualize significantly responsive branches in a phylogenetic tree, while appropriately adjusting for multiple comparisons.
SigTree: A microbial community analysis tool to identify and visualize
significantly responsive branches in a phylogenetic tree
John R. Stevens, Todd R. Jones, Michael Lefevre, Balasubramanian
Ganesan, Bart C. Weimer
PII: S2001-0370(17)30013-2
DOI: doi:10.1016/j.csbj.2017.06.002
Reference: CSBJ 188
To appear in: Computational and Structural Biotechnology Journal
Received date: 7 February 2017
Revised date: 2 June 2017
Accepted date: 28 June 2017
Please cite this article as: Stevens John R., Jones Todd R., Lefevre Michael, Ganesan
Balasubramanian, Weimer Bart C., SigTree: A microbial community analysis tool to
identify and visualize significantly responsive branches in a phylogenetic tree, Computa-
tional and Structural Biotechnology Journal (2017), doi:10.1016/j.csbj.2017.06.002
This is a PDF file of an unedited manuscript that has been accepted for publication.
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SigTree: a microbial community analysis tool to identify and visualize
significantly responsive branches in a phylogenetic tree
John R. Stevens1,*, Todd R. Jones1,2, Michael Lefevre3, Balasubramanian Ganesan4, and Bart C.
1Department of Mathematics and Statistics, Utah State University, Logan, Utah, USA
2Department of Economics, Cornell University, Ithaca, New York, USA
3USTAR Applied Nutrition Research Team, Department of Nutrition, Dietetics, and Food
Science, Utah State University, Logan, Utah, USA
4Western Dairy Center, Department of Nutrition, Dietetics, and Food Science, Utah State
University, Logan, Utah, USA
5School of Veterinary Medicine, University of California, Davis, California, USA
*Corresponding author
3900 Old Main Hill
Utah State University
Logan UT, 84322-3900
TEL: 011-1-435-797-2818; E-mail:
Microbial community analysis experiments to assess the effect of a treatment intervention
(or environmental change) on the relative abundance levels of multiple related microbial species
(or operational taxonomic units) simultaneously using high throughput genomics are becoming
increasingly common. Within the framework of the evolutionary phylogeny of all species
considered in the experiment, this translates to a statistical need to identify the phylogenetic
branches that exhibit a significant consensus response (in terms of operational taxonomic unit
abundance) to the intervention. We present the R software package SigTree, a collection of
flexible tools that make use of meta-analysis methods and regular expressions to identify and
visualize significantly responsive branches in a phylogenetic tree, while appropriately adjusting
for multiple comparisons.
Keywords: microbial informatics; phylogenetic tree; microbial community analysis; microbiome
1. Introduction
The 16S rRNA gene is found in all bacteria, and 16S rRNA sequencing is one of the
high-throughput genomic methods that can be used to both identify bacteria found in a sample,
as well as quantify the abundance levels of individual bacterial species [1]. This is a critical
element of microbial community analysis, and can shed light on complex bacterial community
membership under various environmental conditions. Each bacterial species can be identified by
its operational taxonomic unit (OTU) identifier, and evolutionary relationships among OTUs can
be visualized in a phylogenetic tree.
It is becoming increasingly common to examine the effect of a treatment intervention (or
change in environmental conditions) on the relative abundance levels of hundreds or even
thousands of OTUs simultaneously in a given system. Recently published examples in the field
of environmental microbiology include a study of hundreds of OTUs at each of four sites
corresponding to four time points since deglaciation [2], and a study of hundreds of OTUs in soil
sediment samples under different levels of aerobic methane oxidation [3]. Rather than only
considering the treatment effect on the relative abundance levels of individual OTUs, it is
frequently of interest to identify (and subsequently visualize) branches of a phylogenetic tree
whose member OTUs exhibit a significant consensus abundance response to the treatment. That
is, branches can be identified where there is sufficient statistical evidence that the overall OTU
abundance response to the treatment is in the same direction (up or down) for the OTUs in the
branch, even if some OTUs in the branch show the opposite (or no) direction response. The tree
visualization tools available in FigTree [4], among others, can be enhanced using the
significance results obtained with the software package SigTree, written for the R environment
[5], which we present in this article. We briefly present two examples as case studies, focusing
on their general demonstrative nature rather than specific biological conclusions.
The first example involves the mouse gut microbiome. Ten mice were randomly
assigned to two diets (whole wheat or refined wheat), and the abundance of each of over 500
OTUs of interest was measured using 16S rRNA sequencing. (All animal procedures were
performed with strict adherence to animal welfare guidelines and with oversight and approval by
the Institutional Animal Care and Use Committee at Utah State University; see Methods section
2.3 for additional study details.) For each OTU, a Wilcoxon rank-sum test [6] compared the
OTU's abundance in the two diets.
The second example involves cheese microbial ecology. In a repeated measures design
(across 4 time points), two replicates were obtained under each of two growing conditions (the
presence or absence of the probiotic bacteria Bif-6), with replicates taken from one of two cheese
batches. In each replicate, the abundance of each of nearly 8,700 OTUs was measured using the
G2 PhyloChip; approximately 1,450 of these were of interest. For each OTU, a repeated
measures model was fit using the R package limma [7], with a contrast testing the mean
abundance difference between the presence and absence of Bif-6.
2. Methods
2.1 Statistical Methods
When multiple studies of the same effect have been conducted, the field of meta-analysis
[8] provides statistical methods to combine the results of those studies to arrive at a clearer
understanding of the effect in question. In microbial community analysis experiments such as
the mouse and cheese examples considered here, the same treatment effect has been tested in
multiple OTUs, and so meta-analytic tools can be applied to the OTU-level results to arrive at a
clearer understanding of the treatment effect in specific families of OTUs (or branches of the
phylogenetic tree). For purposes of generalizability and flexibility, we focus on meta-analytic
methods that combine the significance results (or p-values) from the OTUs [9]. Meta-analytic
methods that employ results other than p-values (such as effect size estimates) could be applied,
but their application would require modification for each experiment, while methods using only
p-values allow for an approach fully generalizable to any experimental design, as will be shown.
In both the mouse and cheese examples, interest lies in determining which OTUs (and
their member branches in the phylogenetic tree) are specifically more abundant or less abundant
in the treatment group (T: whole wheat diet or Bif-6 presence) than in the control group (C:
refined wheat diet or Bif-6 absence), rather than just calling OTUs (and member branches)
differentially abundant. For this reason, in testing the null hypothesis µT = µC (where µx is the
mean abundance of the OTU in group x), the alternative hypothesis is of the form µT > µC, thus
producing a one-sided p-value from the respective test (for each OTU). As a result, very small
p-values (close to 0) represent evidence that treatment induces greater abundance than does
control, while very large p-values (close to 1) indicate less abundance in treatment versus
The use of one-sided p-values is less common than two-sided, and deserves an additional
note here regarding interpretation. Let
be the sample mean abundance for a given OTU in
group x. If a one-sided test of null µT = µC vs alternative µT > µC produced a p-value of 0.01,
then the corresponding two-sided test of µT = µC vs alternative µT µC would produce a p-value
of 0.02 (with
 
). Similarly, a one-sided p-value of 0.99 also corresponds to a two-sided p-
value of 0.02 (but with
). For this reason, to control the type I error rate at α=0.05, the
one-sided test requires a p-value less than 0.025 to conclude significant evidence of “µT > µC” ,
and a p-value greater than 0.975 to conclude significant evidence of “µT < µC”. This is
equivalent to performing a two-tailed test, and (when the p-value is less than 0.05) concluding
µT > µC” when
 
, and “µT < µC” when
 
. In addition, within the one-sided test
framework, if the direction of the alternative hypothesis µT > µC were switched to µT < µC, the
one-sided p-value of 0.01 would simply be transformed to 0.99, and the conclusion (regarding
direction of effect) would be unchanged. The SigTree package function p2.p1 converts two-
sided p-values to one-sided p-values, given the corresponding
difference (or its sign).
The most meaningful meta-analytic method to combine the one-sided p-values of all
OTUs in a given branch is Stouffer's method [9,10], which converts the one-sided p-values to
standard normal variates, the weighted sum of which is taken as a standard normal test statistic.
Briefly, if one-sided p-values p1, …, pk correspond to k OTUs in a given branch, these are
transformed to standard normal deviates Z1, …, Zk, where Zi is the value in the standard normal
distribution with upper-tail area pi. Then a statistic ZS =
 is calculated, and the
Stouffer method’s p-value is the upper-tail area from ZS in the standard normal distribution [10].
Stouffer’s method thus produces a single one-sided p-value for the entire branch,
essentially corresponding to a test of H0: the OTUs in the branch have no consensus of
differential abundance vs. Ha: there is a consensus (in a particular direction) of differential
abundance in the branch. The same directional interpretation as in the single-OTU p-value
applies to this branch-level p-value (i.e., a p-value close to 0 suggests branch consensus of
greater abundance in treatment, while a p-value close to 1 suggests branch consensus of less
abundance in treatment). Among meta-analytic p-value combination methods, Stouffer’s method
has been shown to have the most meaningful consensus biological interpretation [11], and also
favorable statistical properties (appropriate Type I error rate control, with higher power and
better precision than competing methods) [12].
An implicit assumption of Stouffer’s method is the independence of p-values to be
combined within a given set of tests (such as a branch of the phylogenetic tree). While it could
be argued that this assumption might be reasonable in some sense (as the p-value for a given
OTU is obtained using only the abundance data for that OTU), there is the potential for some
biologically-based dependence. Specifically, it is possible (and in some cases, perhaps to be
expected) that more closely related OTUs will respond more similarly to the treatment
intervention, resulting in more similar p-values for more closely related OTUs. We test for this
type of dependence with permutational multivariate analysis of variance using distance matrices
[13,14], based on the adonis test of the R package vegan [15]. Briefly, this approach as
implemented in the SigTree package uses a permutation test to evaluate whether the OTU p-
values are independent, or whether differences among the OTU p-values are associated with
between-OTU distances. “Distance” here is phylogenetic distance as represented in the
corresponding phylogenetic tree.
A generalized version of Stouffer’s method that allows for dependence of p-values was
constructed by Hartung [16]. Briefly, and using the same notation as above for the summary of
the Stouffer method, the Hartung method calculates 
, where is one minus
the sample variance of the Z1, …, Zk. Then a statistic
 is
calculated, and the Hartung method’s p-value is the upper-tail area from ZH in the standard
normal distribution [16].
While Hartung’s method assumes a constant correlation among all pairs of p-values, its
results have been shown to be stable even in the presence of a non-constant correlation [16],
which would be the case in the event of a significant adonis test. Accordingly, when the adonis
test indicates significant dependence among p-values in the phylogenetic tree, we recommend
employing Hartung’s meta-analytic method to obtain a p-value for each branch.
Because of the potentially thousands of OTUs and branches being tested simultaneously,
adjustments for multiple comparisons are included in SigTree. The default is to control the
strong family-wise error rate (FWER) using the Hommel p-value adjustment method [17], but
other options exist, including p-value adjustment for false discovery rate (FDR) control while
allowing positive dependence among the many tests [18]. For both error rates, the SigTree
package converts the one-sided p-values to two-sided p-values, applies the p-value correction,
and converts back to one-sided adjusted p-values so that directional interpretation is preserved.
Once the (usually error-rate-adjusted) branch-level p-values have been obtained, the
SigTree package assigns a color (based on p-value range) to each branch and tip in the
phylogenetic tree to aid in visualization. To make this visualization flexible, SigTree can take
user-defined colors and p-value thresholds, and also export the phylogenetic tree in a Nexus
format file [19], with branch colors and p-values embedded via regular expressions [20] since
this format is simply a structured text file. This Nexus file is then read by tree visualization
programs such as FigTree [4]. SigTree can also send the p-value and OTU members for each
branch to a spreadsheet file so that significant changes in the community membership can be
noted and connected to the community and the treatments in a causal association.
2.2 Simulation Study
A simulation study was conducted to evaluate the performance of the SigTree approach.
No simulation could reasonably cover all possible scenarios of tree structure and treatment
effects, and the underlying meta-analytic and statistical methods employed by SigTree have been
previously validated in the literature [8, 10-14, 16]. Instead, the purpose of this simulation was
to serve as a proof of principle that the SigTree approach achieves its intent within the context of
phylogenetic trees, and that its results behave as expected in terms of power and type I error rate
Fig 1 shows the basic tree outline considered in this simulation study. There are four
main subtrees (or branches) subtree A includes OTUs with a positive response to the treatment,
Fig 1. Tree outline used in simulation study.
subtree C includes OTUs with a negative response to the treatment, and subtrees B and D include
OTUs with no response to the treatment. The numbers of OTUs in subtrees A and C were set at
20 and 10, respectively, and the numbers of OTUs in subtrees B and D varied from 2 to 100 (but
with the same numbers of OTUs in both B and D). Fig 1 only shows two OTUs in each subtree,
just for convenience in visualization.
This simulation study assumed the relative abundance of each OTU was measured on
each of 20 subjects, under both treatment and control conditions. For all OTUs, abundances in
control conditions were simulated from a standard Normal(0,1) distribution. For each OTU
exhibiting a response to treatment (as in subtrees A and C of Fig 1), the magnitude of response
was randomly chosen as a uniform value between 0.1 and δ, and OTU abundance in the
treatment condition was simulated as a normal variate with that magnitude mean, and variance 1.
The value δ varied (in ten steps) from 0.1 to 2, and the number of OTUs in subtree B (nB, same
as the number of OTUs in subtree D) varied (also in ten steps) from 2 to 100. At each of the 100
combinations of δ and nB values, over 500 trees (with corresponding subject-level data) were
simulated, and a t-test was used to obtain a raw one-sided p-value for each OTU in each
simulation. The SigTree method was applied to each tree, obtaining Stouffer-combined and
Hommel-adjusted p-values for each of the labeled nodes A-G in Fig 1. The power and type I
error rates (at each combination of δ and nB values) were assessed by taking the proportions
(across simulations) of significant resulting p-values. (Here “significance” includes the
appropriate direction, up or down, in subtrees A and C, respectively. The same direction could be
detected in subtree E as in subtree A, and in subtree F as in subtree C, depending on the relative
sizes of the subtrees and magnitude of possible treatment effect.) The type I error rate was
assessed by the proportion of significant p-values in subtrees B and D, where there was no true
treatment response.
2.3 Example Study Designs
The mouse gut microbiome study was approved as IACUC #1423, and involved
individually housed C57BL/6J mice. DNA was isolated from cecal samples. The V1+V2 125
region of the bacterial 16S rRNA gene was amplified using tag-encoded primers for
pyrosequencing (Roche 454 GS FLX, Branford, CT). The V1-forward primer was 5’-
AGAGTTTGATCCTGGCTCAG (BSF8) and the V2-reverse primer was 5’-
CTGCTGCCTYCCGTA (BSR357). Sequencing was done at the Utah State University Center
for Integrated BioSystems core sequencing facility. The representative sequences were aligned
with PyNAST [21] and a phylogenetic tree was constructed with FastTree [22] after the aligned
sequences were filtered with the default lanemask file and the chimeras were removed.
Microbiota sequences were processed through the data analysis pipeline QIIME [23]. Sequences
were clustered into operational taxonomic units (OTUs) at a 97% sequence similarity with
UCLUST [24].
In the cheese microbial ecology study, Bif-6 is the trademark name of a probiotic
bacterial culture of Bifidobacterium lactis (Cargill Inc., Milwaukee WI). Sample collection and
DNA sequencing followed protocols described in more detail elsewhere [30] and the same
facilities conducted the DNA sequencing on the same equipment. For the Phylochip data, a
phylogenetic tree in Nexus format of the 8,700 OTUs was constructed from available
taxonomical information prior to testing and visualization inside SigTree.
3. Results
3.1 Simulation Study Results
Fig 2 shows as a contour plot the proportions of simulations where the SigTree approach
detected significant consensus response in the lettered subtrees of Fig 1, when the family-wise
error rate was to be controlled at level α=0.05. As would be expected, the proportions (or
statistical power) steadily increase with δ for subtrees A and C, unaffected by nB (the sizes of
subtrees B and D).
For subtrees B and D, which have no OTUs with a true response to treatment, the
proportion of simulations where SigTree detects a significant consensus response would
correspond to a type I error rate. Fig 2 shows this error rate to be less than 0.01 for all
combinations of nB and δ, indicating conservative type I error rate control by SigTree, at least
for this tree structure and treatment effect framework.
For subtrees E-G, the proportions of simulations detecting significant consensus response
Fig 2. Contour plots of proportion of simulations returning significant SigTree results, for
each lettered subtree in Fig 1. nB is the number of OTUs in subtree B (same as number in
subtree D), and δ is the magnitude of maximum possible response in subtrees A and C.
increases with δ (possible magnitude of treatment effect), but less so as nB (the size of subtree B,
as well as D) increases. Essentially, as the sizes of the “null” subtrees (B and D here) increase,
their lack of any response to treatment will eventually drown out the consensus response in
subtrees A and C, so that the higher-level subtrees E-G are more rarely detected as exhibiting a
significant consensus response. However, for larger magnitude response (δ) in subtree A, when
subtree A is a more dominant presence in subtree E (i.e., when nB is smaller), there is greater
evidence of a consensus response in subtree E. This same pattern is seen in subtree F (regarding
the relative size of subtree C therein), and speaks to the general interpretation of a consensus
While limited in scope to this one basic tree structure and treatment effect framework,
this simulation study (as a proof of principle) demonstrates the expected performance of SigTree
appropriate power increase as the magnitude of treatment response increases (in subtrees
exhibiting a consensus response), and appropriate (though possibly conservative) control of the
type I error rate.
It should be noted that these simulations required nearly 6 days’ worth of computation
time, reduced to less than 5 hours real time, thanks to the batch computing resources of the
Center for High Performance Computing at the University of Utah.
3.2 Example Study Results
Fig 3a visualizes the SigTree package results from the mouse gut microbiome example,
with the FWER across the entire phylogenetic tree controlled at 0.05. (See section 2.1’s
discussion of one-sided p-values regarding why the lower and upper thresholds of 0.025 and
0.975 are appropriate here when controlling the error rate at 0.05.) Hartung’s method was used
to obtain the branch-level p-values, as the adonis test showed significant evidence (p-value
0.0012) of distance-based dependence among the OTU-level p-values. The FWER was
controlled across the entire phylogenetic tree using Hommel’s p-value adjustment method. A
Fig 3. Visualization of Significantly Responsive Branches. SigTree visualization of the (a)
mouse gut microbiome and (b) cheese ecology phylogenetic trees, with legend for both trees
showing one-sided p-value ranges on a color scale. Darker red indicates branches of OTUs more
abundant in whole wheat diet than refined wheat (a), and more abundant in the presence of Bif-6
than the absence (b). Darker blue indicates branches less abundant in whole wheat (a).
substantial portion of the phylogenetic tree (indicated by blue branches) is found to demonstrate
a consensus decreased abundance in whole wheat diet compared to refined wheat diet. Because
the FWER is controlled at 0.05, the probability of such a conclusion (for any given branch in the
tree) being false is less than 0.05.
In the cheese microbial ecology example, there was also significant evidence (adonis test
p-value 0.0001) of distance-based dependence among the OTU p-values. Using Hartung’s p-
value combination method, and controlling the FWER with Hommel’s p-value adjustment
method, Fig 3b shows the SigTree package results for this cheese microbial ecology example.
The branch colors here use the same p-value intervals as in the legend of Fig 3a, and these colors
(and the corresponding branch p-values) were embedded in a resulting Nexus file. In Fig 3b, this
Nexus file from SigTree was opened in FigTree [4], where rotation, highlighting, and tip label
suppression were used to facilitate visualization. The dark red branches correspond to families
of OTUs that exhibit an overall significant consensus greater abundance in the presence of Bif-6
than the absence.
While our focus in this presentation is on the general demonstrative nature of the SigTree
package rather than specific biological conclusions, as one example of biologically relevant
outcomes that can be derived from this approach, we comment briefly on the highlighted red
branch of Fig 3b. Many OTUs in this branch represent bacteria that metabolize sulfur in different
environments; sulfur metabolism alters cheese flavor beneficially [29]. Even after controlling
the family-wise error rate across all branches in the entire phylogenetic tree, there is statistically
significant evidence that the presence of Bif-6 results in a consensus increased abundance of this
family (or phylogenetic branch) of bacteria. This could in turn induce more sulfur metabolic
pathways, and thus provide beneficial flavor changes to the cheese.
4. Discussion
SigTree provides tools to use results of OTU-level significance tests (with meaningful
one-sided p-values) to identify and visualize branches in a phylogenetic tree that are significantly
responsive to some treatment intervention or change in environmental conditions. This
functionality is not available in any other software package, and SigTree does much more than
just map data values onto the tree. It provides a convenient interface to a reliable statistical
framework allowing meaningful statements regarding significance of the response not just for
the abundance levels of single OTUs, but for entire branches of the phylogenetic tree.
Two methods in the literature that may appear at least superficially similar to SigTree are
phylofactorization [25] (implemented in R) and Gneiss [26] (implemented in Python).
Phylofactorization identifies “sub-groups of taxa [in a given phylogenetic tree] which respond
differently to treatment relative to one-another.” [25] This is essentially the same objective as
SigTree, and the corresponding phylofactorization implementation requires the use of raw data
(relative abundance of each tip OTU in each sample) and allows multiple covariates. By
comparison, SigTree uses tip-level p-values corresponding to the test of a single effect. While
this may seem to limit the flexibility of SigTree, the opposite is in fact the case the
phylofactorization method is actually designed to test effects only in a multiple regression model,
assuming normal data (after an automated isometric log-ratio transform), and not allowing
random effects or nesting or repeated measures (which are important characteristics in many
study designs, such as the cheese microbial ecology example used here). In contrast, the use of
SigTree allows (actually requires) users to select an appropriate model for their given study
design, including accounting for study-specific data distribution (such as Poisson or negative
binomial for count data from a certain high-throughput technology; or choosing a nonparametric
test as was done for the mouse gut microbiome study here). While SigTree does only look at one
effect at a time in a given model, it can look at multiple effects (or contrasts) from a complex
model, one at a time. This flexibility in the construction of the SigTree approach is intentional,
to allow its application in any experimental design and with any high-throughput technology.
The Gneiss method is designed to “[understand] species distributions across different
covariates” [26], and, like the phylofactorization method [25], employs an isometric log-ratio
(ILR) transform of the raw OTU relative abundance data. While the Gneiss framework
putatively allows for mixed models with multiple covariates, it is left to the user to specify the
model (among those available in Python), and Gneiss is constrained by limitations involving the
ILR. Specifically, raw data values of zero are problematic for Gneiss’s use of the ILR, with the
only current solutions being to add a pseudocount or drop certain OTUs. SigTree also requires
the user to specify the appropriate (and possibly mixed) model for their study design and data,
but prior to using the package’s functions. This actually allows greater flexibility than Gneiss,
such as when it would be meaningful and appropriate to choose a model that explicitly allows
(possibly an abundance of) zeros in the raw abundance data, or when a given model is not readily
fit using tools in (Gneiss-required) Python. In addition, Gneiss does not include convenient tree-
level visualization tools, which are a strength of SigTree.
The default multiplicity correction employed by SigTree is Hommel [17] (for family-wise
error rate control, to allow for stronger conclusion statements). The package also allows control
of the false discovery rate using the Benjamini-Yekutieli correction [18]. Both of these
corrections were selected for inclusion in SigTree because they allow for general positive
dependence among tests [18, 27], and it would be reasonable to expect such dependence among
the many (and sometimes nested) tests within a tree. However, these corrections do not
explicitly account for the dependence possibly induced by the nested structure of the
phylogenetic tree. Accounting for dependence among nested or overlapping tests has been
previously addressed in the context of gene ontology graphs [28], particularly for false discovery
rate control. Adapting such an approach to the phylogenetic tree structure, and addressing family
wise error rate control within the nested tree structure, are possibilities for future SigTree-related
The context of gene ontology graphs actually raises another perspective from which to
consider the type of testing done by SigTree. The branches (or their corresponding nodes) within
a phylogenetic tree are essentially pre-defined groups of OTUs, just as nodes within a gene
ontology graph are pre-defined groups of genes. Where the groups of OTUs are based on
(estimated) phylogeny, the groups of genes are based on (estimated) common roles in terms of
biological processes, molecular functions, or cellular components [31]. The meta-analytic
methods employed by SigTree in the phylogenetic tree context are similar to those employed by
the mvGST approach in the gene ontology context [33]. It may be possible to extend or adapt
other statistical methods for testing these structured groups (or sets) of genes such as GSEA [34]
or ROAST [35], and apply them in the context of a phylogenetic tree. These are also possibilities
for future SigTree-related work.
Both of the example studies presented here demonstrate a strength of SigTree and the
statistically powerful meta-analytic methods it employs. After multiple comparison adjustments,
it may be that no OTU exhibits an individually significant response to the treatment intervention.
For example, all tips are colored grey in Fig 3, indicating no tip-level significant response to the
treatment interventions when the FWER is controlled at 0.05. However, overall tendencies
within branches can be detected and legitimately called statistically significant, due to the power
of the meta-analytic p-value combination methods employed by SigTree. In both of these
examples, this statistical power results in the identification (and subsequent visualization in Fig.
3) of branches that do exhibit significant consensus response to the corresponding treatment
SigTree’s reliance on p-values rather than raw data makes this package flexible for any
experimental design and high-throughput technology, ensuring its long-term utility to microbial
community analysis researchers. SigTree is written for the R environment [5], and results can be
visualized in R as well as in other programs (such as FigTree). SigTree is open source, and
freely available (with a tutorial vignette demonstrating package code usage) at http://cran.r- The current tutorial vignette is also provided with
this manuscript as supplemental file S1. SigTree and its tutorial vignette will be maintained and
updated by the corresponding author as future needs evolve.
SigTree can help microbial community researchers efficiently make and effectively
communicate (in visual form) novel discoveries regarding how the abundance levels of entire
families of OTUs (or branches in the phylogenetic tree) are affected by treatment interventions or
other environmental changes.
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Supplemental Files
S1 File. SigTree tutorial vignette. An overview and demonstration of the main syntax to use
the SigTree software package, with sample data. Also archived (with possible future updates) at (PDF)
... To date, even though microbiome studies frequently repeat analyses at multiple taxonomic levels and tools have been developed capable of highlighting specific important taxa within a tree (e.g. 16 ), it is rare to examine all taxa across a phylogenetic tree in a single analytical framework, enabling the relative importance of community differences at different phylogenetic scales to be assessed. ...
Full-text available
Findings from gut microbiome studies are strongly influenced by both experimental and analytical factors that can unintentionally bias their interpretation. Environment is also critical. Both co-housing and maternal effects are expected to affect microbiomes and have the potential to confound other manipulated factors, such as genetics. We therefore analysed microbiome data from a mouse experiment using littermate controls and tested differences among genotypes (wildtype versus colitis prone- mdr1a −/− ), gut niches (stool versus mucus), host ages (6 versus 18 weeks), social groups (co-housed siblings of different genotypes) and maternal influence. We constructed a 16S phylogenetic tree from bacterial communities, fitting random forest models using all 428,234 clades identified. Models discriminated all criteria except host genotype, where no community differences were found . Host social groups differed in abundant, low-level, taxa whereas intermediate phylogenetic and abundance scales distinguished ages and niches. Thus, a carefully controlled experiment treating evolutionary clades of microbes equivalently without reference to taxonomy, clearly identifies whether and how gut microbial communities are distinct across ecologically important factors (niche and host age) and other experimental factors, notably cage effects and maternal influence. These findings highlight the importance of considering such environmental factors in future microbiome studies.
... The current state of the art includes specialized tools like Anvi'o (10), which consolidates a large collection of methods for sequencebased analysis and visualization of metagenomic assembled-genomes, pangenomes, and proteins (among many other data types). The state of the art also includes more general-purpose tree visualization tools like PHYLOViZ (9), SigTree (11), and iTOL (12) (among many others). Although tree structures are usually stored in standard file formats like Newick, the data accompanying these trees-for example, tip-level taxonomic classifications or other metadata values-are less standardized and sometimes require the onerous creation of configuration files. ...
Full-text available
Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality—including ordination integration and animations—alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of ‘omic data. IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.
... Community-level differences between sample groups can be tested with PERMANOVA and other methods ( Oksanen et al. 2011;Anderson and Walsh 2013) and further complemented by unsupervised analyses (Sankaran and Holmes 2018a;Singh et al. 2019) such as Dirichlet Multinomial Mixtures (DMMs) (Ding and Schloss 2014;Harris et al. 2014). Further tools are available for the analysis of phylogenetic trees ( Paradis et al. 2004;Wright 2016;Stevens et al. 2017;Washburne et al. 2017), co-occurrence networks ( Schwager et al. 2014;Kurtz et al. 2015), metabolic interactions ( Cao et al. 2016), and microbiome function ( Aßhauer et al. 2015). Visualization tools span from amplicon sequencing data (Andersen et al. 2018) to unsupervised ordination by incorporating phylogenetic structure (Fukuyama 2017) to network analysis (Csardi and Nepusz 2006), phylogenetic trees ( Paradis et al. 2004), taxonomic diversity ( Foster et al. 2017), and geospatial analysis (Charlop-Powers and Brady 2015). ...
Best practices from open data science are spreading across research fields, providing new opportunities for research and education. Open data science emphasizes the view that digitalization is enabling new forms of resource sharing, collaboration and outreach. This has the potential to improve the overall transparency and efficiency of research. Microbiome bioinformatics is a rapidly developing area that can greatly benefit from this progress. The concept of microbiome data science refers to the application of best practices from open data science to microbiome bioinformatics. The increasing availability of open data and new opportunities to collaborate online are greatly facilitating the development of this field. A microbiome data science ecosystem combines experimental research data with open data processing and analysis and reproducible tutorials that can also serve as an educational resource. Here, we provide an overview of the current status of microbiome data science from a community developer perspective and propose directions for future development of the field.
Full-text available
Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive tool for visualizing trees in the context of microbiome, metabolome, etc. community data scalable beyond modern large datasets like the Earth Microbiome Project. EMPress provides novel functionality—including ordination integration and animations—alongside many standard tree visualization features, and thus simplifies exploratory analyses of many forms of ‘omic data.
Full-text available
SigTree tutorial vignette. An overview and demonstration of the main syntax to use the SigTree software package, with sample data. Also archived (with possible future updates) at
Full-text available
Marker gene sequencing of microbial communities has generated big datasets of microbial relative abundances varying across environmental conditions, sample sites and treatments. These data often come with putative phylogenies, providing unique opportunities to investigate how shared evolutionary history affects microbial abundance patterns. Here, we present a method to identify the phylogenetic factors driving patterns in microbial community composition. We use the method, “phylofactorization,” to re-analyze datasets from the human body and soil microbial communities, demonstrating how phylofactorization is a dimensionality-reducing tool, an ordination-visualization tool, and an inferential tool for identifying edges in the phylogeny along which putative functional ecological traits may have arisen.
Full-text available
Advances in sequencing technologies have enabled novel insights into microbial niche differentiation, from analyzing environmental samples to understanding human diseases and informing dietary studies. However, identifying the microbial taxa that differentiate these samples can be challenging. These issues stem from the compositional nature of 16S rRNA gene data (or, more generally, taxon or functional gene data); the changes in the relative abundance of one taxon influence the apparent abundances of the others. Here we acknowledge that inferring properties of individual bacteria is a difficult problem and instead introduce the concept of balances to infer meaningful properties of subcommunities, rather than properties of individual species. We show that balances can yield insights about niche differentiation across multiple microbial environments, including soil environments and lung sputum. These techniques have the potential to reshape how we carry out future ecological analyses aimed at revealing differences in relative taxonomic abundances across different samples. IMPORTANCE By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at . Author Video : An author video summary of this article is available.
In combining several tests of significance the individual test statistics are allowed to be dependent. By choosing the weighted inverse normal method for the combination, the dependency of the original test statistics is then characterized by a correlation of the transformed statistics. For this correlation a confidence region, an unbiased estimator and an unbiased estimate of its variance are derived. The combined test statistic is extended to include the case of possibly dependent original test statistics. A simulation study shows the performance of the actual significance level.