Extracted Text

A Guide to Enterotypes across the Human Body: Meta-
Analysis of Microbial Community Structures in Human
Microbiome Datasets
Omry Koren
1.
, Dan Knights
2.
, Antonio Gonzalez
2.
, Levi Waldron
3,4
, Nicola Segata
3
, Rob Knight
5,6
,
Curtis Huttenhower
3
, Ruth E. Ley
1
*
1Department of Microbiology, Cornell University, Ithaca, New York, United States of America,2Department of Computer Science, University of Colorado, Boulder,
Colorado, United States of America,3Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America,4Department of
Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America,5Department of Chemistry and Biochemistry,
University of Colorado, Boulder, Colorado, United States of America,6Howard Hughes Medical Institute, University of Colorado, Boulder, Colorado, United States of
America
Abstract
Recent analyses of human-associated bacterial diversity have categorized individuals into ‘enterotypes’ or clusters based on
the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis
for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of
enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole
genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences
from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT.
In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some
body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all
clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends
not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we
recommend that multiple approaches be used and compared when testing for enterotypes.
Citation:Koren O, Knights D, Gonzalez A, Waldron L, Segata N, et al. (2013) A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial
Community Structures in Human Microbiome Datasets. PLoS Comput Biol 9(1): e1002863. doi:10.1371/journal.pcbi.1002863
Editor:Jonathan A. Eisen, University of California Davis, United States of America
ReceivedMarch 5, 2012;AcceptedNovember 10, 2012;PublishedJanuary 10, 2013
Copyright:2013 Koren et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding:This research was supported by the HMP DACC (HG4866), The Hartwell Foundation (REL), the Arnold and Mabel Beckman Foundation (REL), the David
and Lucile Packard Foundation (REL), the National Science Foundation (DBI-1053486 to CH), the National Institutes of Health (HG4872 to RK and 1R01HG005969 to
CH), Danone (PLF-5972-GD to CH), the Crohns and Colitis Foundation of America (RK), and the Howard Hughes Medical Institute (RK). The funders had no role in
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests:The authors have declared that no competing interests exist.
* E-mail: rel222@cornell.edu
.These authors contributed equally to this work.
Introduction
Together with the MetaHIT consortium [1], the Human
Microbiome Project (HMP) represents one of the first major
attempts to define the microbial diversity comprising the ‘‘normal
healthy’’ human microbiome [2]. The HMP dataset includes 16S
rRNA gene sequence data of roughly twice the size of all similarly
derived data in previously published studies, effectively tripling the
size of combined data available for comparative studies (Table S1).
In addition, the HMP generated whole-genome shotgun (WGS)
metagenomic data for a subset of individuals. These data allowed
for the characterization of patterns of microbial diversity across
body sites and between individuals [2].
The HMP data also provides an opportunity to test the
generality of the concept of enterotypes in the human microbiome.
Arumugamet al.first articulated the concept of enterotypes as
robust clustering of human gut samples based on microbial
community composition, and largely driven by the abundances of
key bacterial genera [3]. Although the term ‘enterotype’ refers to
microbiota types within the gut, the concept can be applied
generally, and here, for convenience, we use the term ‘enterotype’
to refer to microbiota types across different body sites. The HMP
data are ideally suited to test the robustness of the enterotype
concept in multiple body sites, and together with recently
published community-generated datasets, across multiple popula-
tions.
In this report, we combined 16S rRNA gene sequence data
generated using next-generation sequencing by the scientific
community (hereafter, ‘community data’) together with the
MetaHIT WGS data [3] and the recently released HMP 16S
rRNA gene sequence data and WGS data [2]. Because there is
currently no community standard for testing for enterotypes, we
explore how the detection of enterotypes is affected by the
following: clustering methodology, distance metrics, OTU-picking
approaches, sequencing depth (i.e., rarefaction), data type (16S
rRNA vs. WGS), and the specific region of the 16S rRNA gene
sequenced. We find that the emergence of enterotypes is sensitive
to the community structure of communities within each body site,
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and importantly also to the analysis methods employed. Our
comparative analysis of various approaches across datasets informs
the discussion on the technical basis for enterotyping and on how
to interpret enterotype results.
Materials and Methods
16S rRNA Gene Sequence Processing
We constructed a database containing the recently released HMP
16S rRNA gene sequence data [4] and publically available (published)
human microbiome datasets (community data). For inclusion,
community datasets were required to contain a minimum of 25
samples per study and sequences generated using the Roche 454
platform (Table S1). The majority of samples were from healthy
controls; however, a small subset of samples was derived from subjects
that differed from adult healthy subjects due to age (i.e., infants and the
elderly), use of antibiotics, or possible presence of disease (Fig. S1). We
acquired raw SFF files and metadata files containing the unique
identifiers for each sample within a study (barcodes) from the authors
and re-processed the data using the default settings in the Quantitative
Insights Into Microbial Ecology (QIIME) analysis pipeline [5]. For the
majority of samples, quality filtering consisted of rejecting reads
,200 nt and.1000 nt, excluding homopolymer runs.6nt,
accepting 0 barcode corrections and 0 primer mismatches; two
datasets were processed with slightly different screening parameters, as
described in their respective publications [6,7]. When picking
operational taxonomic units (OTUs) we used the OTU tables
generated by the HMP, which were createdde novo. Because the
regions of the 16S rRNA gene differed between studies (and within: the
HMP sequenced both V1–V3 and V3–V5 regions), we used a
reference-based approach (hence,we did not denoise the data) to pick
OTUs at 97% pairwise identity using as a reference the latest release of
the GreenGenes (GG) taxonomy [8]. We also used the phylogenetic
tree from GG to calculate weighted (abundance based) and unweighted
(presence/absence based) UniFrac distances between communities [9],
after applying two rarefactions (1,000 and 2,000 sequences/sample) to
standardize sequence counts. Principal coordinates analysis (PCoA)
was applied to the distance matrices for visualization.
Metagenomic Data Processing
The HMP and MetaHIT shotgun metagenomic datasets were
taxonomically profiled using MetaPhlAn [10] (version 1.1, default
parameter settings), which infers relative abundances for all
taxonomic levels (from phyla to species) for Bacteria and Archaea.
We performed standard quality control on the HMP and
MetaHIT samples as reported in the original studies [2,3] - other
metagenomic pre-processing steps (e.g., error detection, assembly,
or gene annotation) are not required by MetaPhlAn. The
taxonomic profiles of HMP metagenomes are available at
http://www.hmpdacc.org/HMSMCP/, and the MetaHIT pro-
files can be downloaded from http://huttenhower.sph.harvard.
edu/metaphlan/. The 690 HMP metagenomic samples from 7
different body sites can be accessed at http://hmpdacc.org/
HMASM/, from which we used the ‘WGS’ reads (i.e., we did not
use the ‘PGA’ assemblies), collapsing multiple visits from the same
individual into one sample. The 124 fecal samples from MetaHIT
were downloaded from the European Nucleotide Archive (http://
www.ebi.ac.uk/ena/, study accession number ERP000108).
Enterotyping
To evaluate the clustering results in the context of previously
published results reporting enterotypes, we merged publicly
available data (genus relative abundance tables) from MetaHIT
[3] with data for the 16S rRNA-based HMP and non-HMP
samples, based on genus-level taxonomy assignments. We
performed enterotype testing using the relative abundances of
OTUs (rarified at 1,000 sequences/sample for the majority of
analyses, except where effect of rarefaction was tested specifically),
to which we applied five distance metrics: Jensen-Shannon
divergence (JSD), Root Jensen-Shannon divergence (rJSD), Bray-
Curtis (BC), and weighted/unweighted UniFrac distances. For the
calculation of JSD and BC distances, we first binned the counts of
OTUs at the desired level (95% and 97% ID for genus and species
level OTUs, respectively). We used the R ‘‘vegan’’ package [11]
for calculating the Bray-Curtis distance according to this formula
for the distance between samplesjandk, with taxa/OTUs indexed
byi:
d
jk~
P
i
Dxji{xkiD
P
i
xjizxki
Clustering was performed via partitioning around medoids in
the R package ‘‘cluster’’ [12]. We chose the number of clusters
and quality of the resulting clusters by maximizing the prediction
strength (PS) [13] and silhouette index (SI) [14]. We applied a
criterion of$0.90 for PS to signify strong clustering (this implies
that 90% of the data points fall within the cluster and 10% are
outliers). For SI, we used a score of 0.5 for moderate clustering
as described by Wuet al.[15], and$0.75 for strong clustering
(note this is close to the value of 0.71 originally reported for
strong clustering [16]). We performed kernel density estimation
of the global distribution of gut microbial communities using the
R package ‘‘ks’’ [17]. This included automatic inference of
unconstrained (non-diagonal) bandwidth parameters using the
function ‘‘Hscv’’. We also calculated the Calin´ski-Harabasz (CH)
statistic for comparison to PS and SI, using the R ‘fpc’ package
[18]. This package uses the following formula for the CH
statistic [19]:
CH
k~
B(k)(n{k)
W(k)(k{1)
,
where
Author Summary
Recent work has suggested that individuals can be
classified into ‘enterotypes’ based on the abundance of
key bacterial taxa in gut microbial communities. However,
the generality of enterotypes across populations, and the
existence of similar cluster types for other body sites,
remains to be evaluated. We combined the Human
Microbiome Project 16S rRNA gene sequence data and
metagenomes with similar published data to assess the
existence of enterotypes across body sites. We found that
rather than forming enterotypes (note we use this term for
clusters in all body sites), most samples fell into gradients
based on taxonomic abundances of bacteria such as
Bacteroides, although in some body sites there is a bi/multi
modal distribution of samples across gradients. Further-
more, many of the methods used in the analysis (e.g.,
distance metrics and clustering approaches) affected the
likelihood of identifying enterotypes in particular body
habitats. We recommend that multiple approaches be
used and compared when testing for enterotypes.
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W(k)~
X
k
h~11
DC
hD
X
w
i
,w
j
[C
h
d
2
w
i
w
j
,
and
B(k)~
X
n
i,j~1
1
n
d
2
w
i
w
j
{W(k):
In this formula,nis the number of data pointsw,kis the number
of clusters, andC
hrepresents the set of data points in clusterh.
Figure 1. Bacterial diversity clusters by body habitat.A–C: All body sites. The two principal coordinates from the PCoA analysis of the
unweighted UniFrac distances are plotted for (A) HMP data; (B) community data (see Table S1 for list of studies), (C) both datasets combined. Symbol
colors correspond to body sites as indicated on panel A. Panel D shows gut samples (majority are fecal) divided into infants (green), children (blue),
adults (black) and elderly (orange) samples. The variance explained by the PCs is indicated in parentheses on the axes.
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Figure 2. A positive control of cluster structure recovered from lognormally distributed synthetic community data containing four
clusters.Presence of enterotypes was tested using: (A) prediction strength, (B) silhouette index and (C) Calin´ski-Harabasz combined with BC, JSD and
rJSD distance metrics. Bars are standard errors.
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We chose to use PS and SI to assess the support for clustering
(and to choose the number of clusters if supported), as they are
both absolute measures of the clustering quality, while CH is
only a relative assessment of the quality of the clustering.
Synthetic Dataset
We generated a synthetic dataset of 100 communities each
containing 3,000 ‘‘sequences’’ belonging to 500 mock OTUs.
For each synthetic community, 90% (2,700 sequences, or OTU
observations) was drawn from the same randomly generated
lognormal abundance distribution (shared across all communi-
ties) and the remaining 10% (300 sequences) drawn from one of
four unique lognormal distributions, forcing the data into four
clusters. We then applied the enterotyping methods as described
above.
Results and Discussion
Mapping HMP Diversity onto Community-Generated
Diversity
Beta-diversity measures provide a view of how diversity differs
between sets of samples and quantifies those differences. We used
the unweighted UniFrac measure ofb-diversity to contrast the
range of bacterial phylogenetic diversity captured by the HMP
data to existing community data (Fig. 1). This analysis showed that
the overall pattern of diversity is similar for HMP and community
data, with clear separation between body sites (Figs. 1A, B, S1) as
has been described previously [2,20]. Similarly, Fig. S2 shows the
locations of the MetaHIT samples relative to the HMP and other
community fecal samples. The HMP and MetaHIT data map
onto the community data well (Fig. 1C; Fig. S2), lending support
Figure 3. Clustering scores for enterotypes in fecal samples using 16S rRNA data.(A) Prediction strength scores, (B) Calin´ski-Harabasz and
(C) average silhouette scores calculated using 5 distances metrics for HMP data only, adult community data, and combined HMP and adult
community data. The thresholds for significance of clustering scores are indicated as dashed lines on the plots. Bars are standard errors.
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for the approach of combining these sets in a meta-analysis of body
habitats.
The gut microbiota are the most extensively studied of the
human-associated microbiota. Combining community and HMP
fecal microbial 16S rRNA gene sequence data effectively extended
the subject age range from early infancy to old age (3 days to 85
years old), with the HMP supplying the majority of the middle
years of the human life span. Interestingly, infant samples (younger
than 2.5 years) were outliers in the range of diversity represented
by the healthy adult and elder (older than 70 years) gut (Fig. 1D)
and were more similar to vaginal and skin communities. Adult
HMP samples cluster together with those from the community
studies, excluding samples from infants (,2.5 yrs) and elders
(Table S1). This combined analysis corroborates the previously
described vast difference between bacterial diversity of infants and
adults [21,22].
Effect of Clustering Methodology
We first tested the effects of different cluster scoring methods
using a lognormally distributed synthetic community data
containing 4 clusters that served as a positive control for
enterotypes. We applied the JSD, rJSD and BC distance measures
Figure 4. Enterotypes in mid vaginal samples in both the HMP and the Ravelet al.[26]datasets.Prediction strength scores calculated
using 5 distances metrics for HMP mid vaginal samples at the genus level (A), Ravelet al. mid vaginal samples at the genus level (B). HMP mid vaginal
samples at the species level (C) and Ravelet al. mid vaginal samples at the species level (D). The thresholds for significance of clustering scores are
indicated as dashed lines on the plots. Bars are standard errors.
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to the synthetic dataset and compared cluster scores using
prediction strength (PS, Fig. 2A), silhouette index (SI, Fig. 2B)
and Calin´ksi-Harabasz (CH, Fig. 2C) scores. This analysis
revealed strong support for 4 clusters using PS for the BC, JSD
and rJSD distance metrics, but SI provided no support for
clustering using BC and rJSD, and only weak support for 3–5
clusters using the JSD distance metric. The CH index supported 4
clusters using only the JSD distance metric. Wuet al.also reported
a discrepancy in cluster scoring strengths between clustering
methods [15]: CH indicated that 3 enterotypes were present, but
SI provided weak support using rJSD. Wuet al.also compared
clustering with CH and SI together with weighted/normalized
unweighted UniFrac, BC and Euclidean distances, and reported
concordant numbers of clusters with weighted UniFrac only.
Together these results indicate that these different clustering
methodologies can yield inconsistent results, although SI and CH
have been reported to be stable and comparable [23,24].
Arumuganet al.used CH as the basis for choosing the number
of enterotypes, even when SI values were very low (all published
values were less than or equal to 0.25), indicating weak or no
support for clustering [3]. It is important to note that the CH score
is a relative measure that alone cannot be used to determine
statistical significance of clustering in the data, and that
furthermore, CH is intended to indicate the optimal number of
clusters based on the assumption that clusters exist. PS and SI, on
the other hand, are absolute measures of how likely cluster
structure is to emerge from a dataset. Based on our results, we
recommend using at least one absolute measure (specifically, we
recommend PS), and if possible confirming those results with an
additional absolute measure (such as SI), when searching for
enterotypes. Depending on the signal-to-noise distribution within
individual datasets and data types, PS may have difficulty
identifying clusters represented by few samples, as we discuss
below (e.g., posterior fornix WGS data). In such cases SI may be
relied on, but we recommend using a high threshold (e.g.,$0.75)
in identifying potentially reproducible clusters. We prefer PS over
SI for large sample sizes because (1) it has a clear quantitative and
intuitive interpretation, (2) it allows estimation of the clustering
stability of individual samples, and (3) it performs better than SI in
recovering known enterotypes in synthetic datasets. Note however
Figure 5. A comparison of prediction scores using different OTU picking methods.Prediction strength scores were calculated with JSD at
2 clusters using either OTUs generated using a reference-based approach orde novo.
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that there is currently no consensus in the field on the specific
thresholds that should be used with these methods for assessing
clustering strength, making it all the more important for
researchers to clearly state the criteria they apply when reporting
enterotypes.
Effect of Distance Metric
We searched for fecal enterotypes in the HMP and community
16S rRNA gene sequence data using the relative abundances of
OTUs across samples, and applying five different distance metrics:
JSD, rJSD, BC, and weighted/unweighted UniFrac distances, and
three cluster evaluation methods (PS, CH and SI; Fig. 3). Using
PS, we observed at best moderate support for 2 fecal enterotypes
in the HMP data using weighted UniFrac, but little or no support
using other distance metrics (Fig. 3A). We obtained similar results
using community data alone and when combined together
(Fig. 3A). Weighted UniFrac scoring for enterotypes was weak
with SI (Fig. 3). Figs. S3, S4, S5, S6, S7 show similar analyses for 3
Figure 6. Prediction scores for enterotypes in fecal samples using WGS data.Prediction strength scores calculated using 3 distances
metrics for (A) HMP, (B) MetaHIT and (C) HMP+MetaHIT data. The thresholds for significance of clustering scores are indicated as dashed lines on the
plots. Bars are standard errors.
doi:10.1371/journal.pcbi.1002863.g006
Figure 7. Gradients of OTU abundances are evident in the combined dataset of fecal samples.HMP and community fecal samples are
shown in a PCoA of weighted UniFrac distances. Samples are colored according to (A) putative cluster membership and by their abundances (0–1, see
legend inserts) of (B)Bacteroides, (C)Faecalibacteriumand (D)Prevotella.
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different vaginal sites, and 9 oral and 3 skin sites. Moderate to
strong clustering is evident in only 3 out of these 15 body sites. In
the mid vagina there is strong support for 2 clusters (discussed
below) using BC, JSD and rJSD (Fig. S3; weighted UniFrac
provided moderate support; unweighted UniFrac provided no
support). In the posterior fornix (Fig. S3) and the attached
keratinized gingiva (Fig. S4), we observed moderate support for 2
clusters using 5 and 4 distance metrics, respectively (unweighted
UniFrac resulted in little or no support in the gingiva).
These results indicate that the detection of enterotypes is
sensitive to the distance metric used, a result also recently
reported by Claessonet al. [25]. Note that this sensitivity is not
dependent on body site. However, because enterotyping is driven
by the relative abundances of specific genera within samples,
unweighted UniFrac, which takes into account presence/absence
of tree branches but not abundances of sequences mapping to
those branches, may not be an ideal distance measure to use for
enterotyping. We include it here because it is widely used in
microbiome studies. In contrast, the weighted UniFrac, BC, JSD
and rJSD distance metrics are based on OTU abundances and
should in principle be more appropriate. The lack of concor-
dance between results based on different abundance-based
distance metrics raises the following questions: if enterotypes
are to be considered robust, must they be observed using more
than one distance metric? Or does the lack of concordance
between results using different distance metrics indicate that
(here at least) weighted UniFrac is the best choice for
enterotyping? Because the interpretation of the findings is
currently subjective, and in the absence of any community-wide
best practices, we recommend using at least 2 or 3 distance
metrics and clearly stating the criteria used for calling
enterotypes within the context of any particular study. Partic-
ularly, if different metrics yield different results, authors should
attempt to understand the discrepancies and justify their choice
of distance metric.
Effect of OTU Taxonomic Level
The effects of OTU taxonomic levels (for instance, clustering
sequences at genus or species level) on the recovery of enterotypes
are best illustrated with 16S rRNA gene sequence data from
vaginal sites (Figs. 4 and S3). Ravelet al.[26] reported enterotypes
in the vagina based on the abundances of bacterial species (as
opposed to genera used in gut studies). We used the abundances of
both species and genus-level OTUs from the Ravelet al.dataset to
test for enterotypes. Our analysis shows strong support for two
genus-level enterotypes using 4 of 5 distance metrics (i.e.,
unweighted UniFrac had moderate support) for the Ravelet al.
dataset when using the PS to test the strength of the clustering
(Fig. 4). We also observed strong support for genus-level mid-
vaginal enterotypes using 3 of 5 distance metrics (BC, JSD and
rJSD) for the HMP dataset (Fig. 4). Additionally, using a species-
level analysis, we obtained moderate support for five enterotypes
using BC and JSD in the Ravelet al.data (we also scored strong
support for 2 enterotypes with weighted UniFrac), and moderate
to strong support for 2 clusters (i.e., little or no support for five
clusters) in the HMP data (Fig. 4).
We also tested for clustering of vaginal samples using SI and
CH. When using SI (Fig. S8) at the genus level we found strong
support for 2 clusters in the HMP and Ravelet al.datasets using 3
and 1 distance metrics respectively. But when using CH (Fig. S9)
on the HMP data at the genus level, the highest scores were
obtained for 2–3 and 9–10 clusters, and in the Ravel data the
strongest support was for 2 clusters. At species level, we observed
strong support with SI for 2 enterotypes in the HMP data using
weighted UniFrac, and for 5 enterotypes using JSD (Fig. S8). No
strong support was observed for the Ravel data for any number of
clusters at the species level using SI. With CH, at the species level,
the highest score was for 10 clusters in the HMP data, while for the
Ravel data the highest score was for 2 clusters. The differences in
number of enterotypes found at the genus and species levels
underscore the sensitivity of enterotyping to the taxonomic depths
used in constructing OTUs.
Effect of 16S rRNA Variable Region
To test for the influence of the specific variable region of the
16S rRNA gene on the detection of fecal enterotypes, we
compared fecal samples from the HMP for which sequence data
for both the V1–V3 and V3–V5 regions were available. Data from
the V3–V5 region yielded moderate support for two fecal
enterotypes, but no enterotypes were detected using data from
the V1–V3 region. When using SI, we observed moderate support
for 2 clusters when using JSD on the V1–V3 data and weak
support for the V3–V5 data. The highest scores using CH were
three clusters using BC for V1–V3 data and two clusters using
weighted UniFrac for V3–V5 data (Fig. S10). Different primers
amplifying different regions of the 16S rRNA gene sequence are
known to impact the diversity described for a microbial
community. For example, primers for the V1–V3 region (e.g.,
27F-338R) are not efficient for amplifying 16S rRNA gene
sequences from members of theBifidobacteriagenus, which can
dominate the infant microbiota [22,27]. Our analysis demon-
strates that the specific region of the 16S rRNA gene that is
amplified during PCR is another factor that can affect the
outcome when searching for enterotypes.
Effect of OTU-Picking Method
We compared enterotype clustering using two methods for
OTU picking: (1)de novosequence clustering into OTUs, in which
sequences are clustered based on similarity to one another, and (2)
a reference based approach, in which sequences are clustered
based on similarity to sequences in a reference database [27]. We
found that for the HMP dataset, the two OTU picking approaches
yielded consistent results for the majority of body sites (Fig. 5).
However, for the attached keratinized gingiva, posterior fornix
and tongue dorsum, the reference-based approach provided
moderate support for enterotypes, whereas thede novoapproach
did not support clustering. One important difference between the
two OTU-picking approaches is that the reference-based method
can yield fewer OTUs, particularly at fine taxonomic resolution,
because any sequence that fails to find a match in the database is
discarded. In contrast, thede novoapproach retains all sequences
and has the potential to yield higher OTU counts. Fewer OTUs
Figure 8. The fecal microbiota exhibit a smooth gradient ofBacteroidesabundances across samples from the HMP and community
studies.Bacteroidesabundances are mapped onto the first two principal coordinates of the weighted UniFrac PCoA analysis for HMP data (A),
community data (B), and combined HMP and community data (C). Left panels: 3D plots showing kernel density estimates mapped onto PC1 and PC2;
Right panels: contours indicate sample densities, sample colors indicateBacteroidesrelative abundances ranging from 0–1, where 1 = 100%
Bacteroides; color levels are determined by quantiles to allow visual comparison of any distribution of relative abundances (e.g., 0% of samples fall
below the first threshold, 20% below the second threshold, 40% below the third, etc.)
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would have the effect of increasing the relative abundances of the
dominant genera, and may therefore strengthen the gradient effect
frequently observed (see below). Thus, the reference-based OTU
picking approach may result in over-confidence in enterotype
discovery.
Effect of Sequence Rarefaction Depth
Rarefaction is the commonly used normalization practice of
randomly subsampling the data so that an equal number of
sequences are drawn for each sample. We rarefied the sequences
from the HMP fecal samples at 2,000 sequences per sample and
compared the results to those obtained after rarefying at 1,000
sequences per sample (Fig. S11). Rarefaction depth did not seem
to strongly affect the results of the clustering.
Effect of Data Type: WGS versus 16S rRNA Gene
Sequences
We also implemented our methodology in the smaller set of
HMP samples for which WGS data were available, in addition to
the MetaHIT WGS data [3]. While the HMP WGS data included
fewer samples and body sites than the 16S rRNA gene sequence
data (approximately 700 spanning the gut, nares, three oral
habitats, and posterior fornix), they provided consistent species-
level resolution. We found a strong gradient effect in the fecal
samples (see discussion on gradients below) for almost all genera
and species, and between species within the genusBacteroidesand
members of the Firmicutes. We also found that the presence of
Prevotella(specifically,P. copri) was clearly associated with the first
principal coordinate in the PCoA using three distance measures.
This feature in turn drove moderate support for two clusters in the
HMP data (using JSD and rJSD) and strong support for 2 clusters
in the MetaHIT data (Figs. 6 and S12), that appeared to separate
roughly according to presence/absence ofPrevotella.
Prevotellais similarly influential in driving variation along the first
principal coordinate axis when using Jensen-Shannon divergence
for the 16S-based samples, albeit not with weighted UniFrac (Figs.
S13-S14). Although the importance ofPrevotellain clustering
analysis clearly depends on the choice of distance metric, the genus
does exhibit enterotype-like behavior in that it follows a bimodal
distribution: high relative abundance in a small fraction of
samples, but low or zero relative abundance in many other
samples. Note that the HMP 16S rRNA gene sequence surveys
include a smaller fraction of samples containing high relative
abundance ofPrevotellacompared to WGS data (12.3% and 10.9%
of samples contained.10%Prevotellain the HMP V1–V3 and
HMP V3–V5 data sets, respectively, compared to 13.9% and
24.2% in the HMP and MetaHIT shotgun metagenomics).
Although a bias of certain primers againstPrevotellain 16S surveys
has been reported previously [28], this is not likely to have affected
the HMP data. The difference inPrevotellaabundance between
MetaHIT and HMP samples remains to be explained.
In all other body sites, we again found general agreement
between metagenomics clustering results and the 16S rRNA gene
sequence-based clustering results regarding cluster quality (Figs.
S15-S16), with the exception of the moderate support for two
enterotypes in the buccal mucosa WGS data (Fig. S15), and lack of
consistent support for enterotypes in the posterior fornix (Fig. S16).
As we described above, we found moderate support for
enterotypes in the posterior fornix in 16S data (Fig. S3). The
discrepancy might be due to the fact that the WGS data included
Figure 9. HMP fecal samples are slightly enriched inBacteroides
abundances compared to community samples. The projection
from Fig. 8C, right panel, is colored to show if samples originate from
the HMP (blue) or community (yellow), and all PC combinations are
shown. See Fig. 8 for description of the axes.
doi:10.1371/journal.pcbi.1002863.g009
Enterotypes across the Human Body
PLOS Computational Biology | www.ploscompbiol.org 12 January 2013 | Volume 9 | Issue 1 | e1002863

too few samples from the smaller ‘‘clusters’’ to permit detection by
the prediction strength approach; SI was highest for two
enterotypes at the genus level corresponding toLactobacillus(either
dominant or absent; JSD jackknifed SI: 0.8960.012), and
statistically tied as highest for five (fourLactobacillusspecies or
Lactobacillusabsent, JSD jackknifed SI: 0.7960.005) and eight (JSD
jackknifed SI: 0.7960.008) enterotypes at the species level (Fig.
S17).
Gradients of OTU Abundances
Bacteroidetes/Firmicutes in gut samples. Gut entero-
types would be apparent if the underlying data contained sharp
divisions in the distribution of samples based on genus-level
abundances (i.e., clear regions with no samples between the
modes in PCoA plots). We observed a smooth gradient
distribution of HMP+community samples based on their
Bacteroidesabundances (Fig. 7), which in turn was the major
determinant of inter-individualb-diversity patterns (Fig. 8).
Samples at the extremes of this gradient were highly enriched
in or depleted ofBacteroides, but such extremes can be
interpretable as outliers on a continuum, rather than statistically
significant groups. To further assess the possibility that this
gradient was bi- or multi-modal, we mapped the density of
samples along the first two dimensions of weighted UniFrac
PCoA (Fig. 8). The kernel density estimates for samples mapped
on principal coordinates 1 and 2 show two peaks emerging from
the community data (but not from HMP data), which contributed
to a second, low peak in the combined data (Fig. 8). The HMP
samples tended to have higherBacteroides-abundance when
compared to the community samples (Fig. 9).
The vast majority of samples occupies the intermediate
region between low- and high-Bacteroidesindividuals and forms
a smooth distribution of gut community configurations (Fig. 8).
Similar gradients ofBacteroidesabundance across samples have
been observed in other studies [3,15]. The factors that drive the
bimodal distribution of samples at ends of the gradient remain
to be determined. Distinctive boundaries of gut enterotypes in
small populations are likely to be blurred with the addition of
samples containing intermediate levels ofBacteroides,although
in some cases they may reflect host and environmental
influences.
The extent to which a subject’s microbiota varies along the
gradient with time is not yet understood. Although many studies
to date suggest relative stability of microbial communities over
time within adult subjects [20,29,30,31], the relative abundance
ofBacteroideshas been shown to vary over time within individuals,
for instance, changes inBacteroidesabundances were reported over
the course of a year-long weight loss study in obese individuals
[30].
Recent work has also revealed an important role for nutrient
status in determining the abundance ofBacteroides. Fasting (in
mice), and feeding (in pythons) can alter the relative abundance of
Bacteroidesquite rapidly [32,33]. In humans, excess energy intake
above that needed for weight maintenance has been shown to
reduceBacteroideslevels [34]. Furthermore, long-term dietary
habits have been linked to enterotypes [15].
The lowBacteroides-abundance samples within the HMP+
community dataset includeda rare subset (total of 6 adult
samples and 7 infant/child samples) with a high abundance
($0.6) ofPrevotella(Fig. S18), which was notable due to generally
low abundance of this genus in most other samples (90.1% of
adults had,10%Prevotella, 64.8% had none in V3–V5 region
data rarefied at 1,000 sequences/sample). Samples with high
Prevotellaabundances have previously been observed in non-
Western human populations [6]. This community pattern has
also been suggested to associate with high-carbohydrate diets
[15]. It is likely that the size of thisPrevotellapeak will increase
with the addition of more data from diverse populations,
especially given the fact that 7 of the 13Prevotella-rich samples
noted above came from non-western subjects. ThisPrevotella-
dominant community, belonging to a distinct group of samples,
was the most enterotype-like cluster we observed in the fecal
samples.
Other body sites.We next looked for enterotypes within the
oral samples, which had the greatest degree of beta-diversity and
were also the richest (highesta-diversity) within the HMP
population [2]. The 9 oral sites also contained gradients of
OTU relative abundances (Figs. S19-S27). These tremendous
ranges of phylogenetic diversity occurred in continua among
individuals and did not provide strong support for discrete
enterotypes (Fig. S4-S6), although in the attached keratinized
gingiva we saw moderate support for two enterotypes using
weighted UniFrac, JSD, rJSD and BC (Fig. S4).
We observed the same pattern was observed for the skin sites.
Each of the three skin types contained typically one or two
dominant OTUs that accounted for the majority of the genus-level
abundances [2]. Our analysis did not detect any enterotypes in the
skin sites (retroauricular creases, antecubital fossae and anterior
nares; Fig. S7), as these communities contained gradients of genus
abundances across samples (Figs. S28-S30).
Table 1.Summary of the factors that may affect enterotyping.
Strength Effect No effect
Known effects Strong Distance metric Rarefaction
Strong Cluster scoring method
Strong Taxonomical level
Moderate Variable region of the 16S rRNA
Weak 16S rRNA vs. WGS data
Weak OTU picking method
Potential effects to be tested Sample processing method
Batch effects
PCR conditions
Study size
doi:10.1371/journal.pcbi.1002863.t001
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PLOS Computational Biology | www.ploscompbiol.org 13 January 2013 | Volume 9 | Issue 1 | e1002863

Prospectus
Our results underscore the importance of methodology in
assessing whether populations can be categorized by enterotypes.
Table 1 summarizes the factors that might have an effect on
enterotyping: the two with the largest effect are the distance
metric and the clustering score method. We recommend using at
least one absolute scoring method (i.e., PS or SI) combined with at
least 2–3 distance metrics to verify the presence of enterotypes.
When using the different scoring methods, authors should
indicate and justify the choice of thresholds for indicating levels
of support for enterotypes. Other factors that should be kept in
mind are the data type (and if using 16S rRNA gene sequence
data, the variable region sequenced) and the OTU picking
method. At the present time, there is no community consensus on
how to define an enterotype, and two researchers with the same
data can easily come to opposite conclusions regarding the
presence of enterotypes if they apply different criteria. Microbial
ecologists and clinicians interested in the enterotype concept need
to standardize enterotyping methods for the concept to gain
utility.
The large size of the HMP dataset, augmented with the
community and MetaHIT data, brought to light the extent of
bacterial abundance gradients within body habitats. The presence
of these gradients underscores that discrete enterotypes (i.e.,
enterotypes with distinct boundaries) are lacking. Instead, for
continuous OTU and genus gradients are the norm for most body
sites, although a few body sites had multimodal distributions of
samples with modes near the extremes of the gradients, and very
few cases (e.g., the vagina) had consistent discrete community types.
The biological drivers of these patterns, and their robustness over
time, may be manifestations of host-microbial interactions,
especially if they correlate with host factors such as diet, lifestyle,
or genetics.
Supporting Information
Figure S1Health state for the subjects whose samples are shown
in Fig. 1. (A) All samples from healthy subjects, for all body sites.
(B) Samples from individuals with health problems, or other
factors that may influence the diversity of the microbiota (i.e.,
smoking, use of antibiotics). (C) Combined data from panels A and
B. For body site legend, see Fig. 1.
(TIFF)
Figure S2The relative locations of the different studies
containing gut samples in PCoA plots of weighted UniFrac and
Jensen-Shannon distances (For study names see Table S1).
(TIFF)
Figure S3Prediction strength scores for enterotypes in HMP (A)
mid vagina, (B) posterior fornix, and (C) vaginal introitus samples.
Prediction strength scores calculated using 5 distances metrics.
The thresholds for significance of clustering scores are indicated as
dashed lines on the plots. Bars are standard errors.
(ZIP)
Figure S4Prediction strength scores for enterotypes in HMP (A)
subgingival plaque, (B) supragingival plaque, and (C) attached
keratinized gingiva samples. Prediction strength scores calculated
using 5 distances metrics. The thresholds for significance of
clustering scores are indicated as dashed lines on the plots.
(TIFF)
Figure S5Prediction strength scores for enterotypes in HMP (A)
buccal mucosa, (B) hard palate, and (C) tongue dorsum samples.
Prediction strength scores calculated using 5 distances metrics.
The thresholds for significance of clustering scores are indicated as
dashed lines on the plots. Bars are standard errors.
(TIFF)
Figure S6Prediction strength scores for enterotypes in HMP (A)
saliva, (B) palatine tonsils, and (C) throat samples. Prediction
strength scores calculated using 5 distances metrics. The thresholds
for significance of clustering scores are indicated as dashed lines on
the plots. Bars are standard errors.
(TIFF)
Figure S7Prediction strength scores for enterotypes in HMP (A)
retroauricular crease, (B) antecubital fossa, and (C) anterior nares
samples. Prediction strength scores calculated using 5 distances
metrics. The thresholds for significance of clustering scores are
indicated as dashed lines on the plots. Bars are standard errors.
(TIFF)
Figure S8Enterotypes in mid vaginal sites samples in both the
HMP and the Ravelet al. [26] datasets. Average silhouette width
scores calculated using 5 distances metrics for HMP mid vaginal
samples at the genus level, Ravelet al. mid vaginal samples at the
genus level, HMP mid vaginal samples at the species level, Ravelet
al. mid vaginal samples at the species level. The thresholds for
significance of clustering scores are indicated as dashed lines on
the plots. Bars are standard errors.
(TIFF)
Figure S9Enterotypes in mid vaginal sites samples in both the
HMP and the Ravelet al. [26] datasets. Calin´ski-Harabasz scores
calculated using 5 distances metrics for HMP mid vaginal samples
at the genus level, Ravelet al. mid vaginal samples at the genus
level, HMP mid vaginal samples at the species level, Ravelet al.
mid vaginal samples at the species level. Bars are standard errors.
(TIFF)
Figure S10Clustering scores for fecal enterotypes in data from
(A) V1–V3 and (B) V3–V5 variable regions of the16S rRNA gene
using PS, SI and CH. Clustering scores calculated using 5
distances metrics. The thresholds for significance of clustering
scores are indicated as dashed lines on the plots. Bars are standard
errors.
(TIFF)
Figure S11Prediction scores for enterotypes in HMP fecal
samples using 16S rRNA data and rarefying at (A) 1,000 and (B)
2,000 sequences per sample. Prediction strength scores calculated
using 5 distances metrics. The thresholds for significance of
clustering scores are indicated as dashed lines on the plots. Bars
are standard errors.
(TIFF)
Figure S12Clustering scores for enterotypes in MetaHIT fecal
samples using WGS data. (A) Prediction strength scores, (B)
average silhouette scores and (C) Calin´ski-Harabasz calculated
using 3 distances metrics. The thresholds for significance of
clustering scores are indicated as dashed lines on the plots. Bars
are standard errors.
(TIFF)
Figure S13Gradients of the 24 taxa most highly correlated with
the first 2 PCs in HMP fecal samples using Jensen-Shannon
divergence for the V3–V5 16S-based data.
(TIFF)
Figure S14Gradients of the 24 taxa most highly correlated with
the first 2 PCs in HMP fecal samples using weighted UniFrac for
the V3–V5 16S-based data.
(TIFF)
Enterotypes across the Human Body
PLOS Computational Biology | www.ploscompbiol.org 14 January 2013 | Volume 9 | Issue 1 | e1002863

Figure S15Prediction scores for enterotypes in HMP samples
using WGS data. Prediction strength scores calculated using 3
distances metrics for buccal mucosa (A), supragingival (B), and
tongue dorsum (C) samples. The thresholds for significance of
clustering scores are indicated as dashed lines on the plots. Bars
are standard errors.
(TIFF)
Figure S16Prediction scores for enterotypes in HMP samples
using WGS data. Prediction strength scores calculated using 3
distances metrics for anterior nares (A), and posterior fornix (B)
samples. The thresholds for significance of clustering scores are
indicated as dashed lines on the plots. Bars are standard errors.
(TIFF)
Figure S17Silhouette index for enterotypes in HMP vaginal
samples using WGS data at different taxonomic levels. Silhouette
index scores calculated using 3 distances metrics for genus (A), and
species (B) levels. The thresholds for significance of clustering
scores are indicated as dashed lines on the plots. Bars are standard
errors.
(TIFF)
Figure S18Taxon distribution for the 10 most common genera
in the small number (13) of highPrevotellasamples ($0.6 relative
abundance).
(TIFF)
Figure S19Gradients ofPrevotella,FusobacteriumandTreponema
abundances in subgingival plaque samples. HMP samples are
shown in a principal coordinates analysis of unweighted UniFrac
distances. Samples are colored according to (A) putative cluster
membership and by their abundances (0–1, see legend inserts) of
(B)Prevotella, (C)Fusobacteriumand (D)Treponema.
(TIFF)
Figure S20Gradients ofSelenomonas,StreptococcusandLeptotrichia
abundances in supragingival plaque samples. HMP samples are
shown in a principal coordinates analysis of unweighted UniFrac
distances. Samples are colored according to (A) putative cluster
membership and by their abundances (0–1, see legend inserts) of
(B)Selenomonas, (C)Streptococcusand (D)Leptotrichia.
(TIFF)
Figure S21Gradients ofPrevotella,StreptococcusandPorphyromonas
abundances in attached keratinized gingiva samples. HMP
samples are shown in a principal coordinates analysis of
unweighted UniFrac distances. Samples are colored according to
(A) putative cluster membership and by their abundances (0–1, see
legend inserts) of (B)Prevotella, (C)Streptococcusand (D)Porphyr-
omonas.
(TIFF)
Figure S22Gradients ofHaemophilus,StreptococcusandVeillonella
abundances in buccal mucosa samples. HMP samples are shown
in a principal coordinates analysis of unweighted UniFrac
distances. Samples are colored according to (A) putative cluster
membership and by their abundances (0–1, see legend inserts) of
(B)Haemophilus, (C)Streptococcusand (D)Veillonella.
(TIFF)
Figure S23Gradients ofStreptococcus,PrevotellaandPorphyromonas
abundances in hard palate samples. HMP samples are shown in a
principal coordinates analysis of unweighted UniFrac distances.
Samples are colored according to (A) putative cluster membership
and by their abundances (0–1, see legend inserts) of (B)Streptococcus,
(C)Prevotellaand (D)Porphyromonas.
(TIFF)
Figure S24Gradients ofStreptococcus,PorphyromonasandAtopobium
abundances in tongue dorsum samples. HMP samples are shown
in a principal coordinates analysis of unweighted UniFrac
distances. Samples are colored according to (A) putative cluster
membership and by their abundances (0–1, see legend inserts) of
(B)Streptococcus, (C)Porphyromonasand (D)Atopobium.
(TIFF)
Figure S25Gradients ofPrevotella, Unclassified Veillonellaceae
andStreptococcusabundances in saliva samples. HMP samples are
shown in a principal coordinates analysis of unweighted
UniFrac distances. Samplesare colored according to (A)
putative cluster membership and by their abundances (0–1,
see legend inserts) of (B)Prevotella, (C) Unclassified Veillonella-
ceae and (D)Streptococcus.
(TIFF)
Figure S26Gradients ofPrevotella,StreptococcusandPorphyromonas
abundances in palatine tonsils samples. HMP samples are shown
in a principal coordinates analysis of unweighted UniFrac
distances. Samples are colored according to their (A) putative
cluster membership and by abundances (0–1, see legend inserts) of
(B)Prevotella, (C)Streptococcusand (D)Porphyromonas.
(TIFF)
Figure S27Gradients ofPrevotella,Neisseriaand Unclassified
Veillonellaceae abundances inthroat samples. HMP samples
are shown in a principal coordinates analysis of unweighted
UniFrac distances. Samplesare colored according to (A)
putative cluster membership and by their abundances (0–1,
see legend inserts) of (B)Prevotella,(C)Neisseriaand (D)
Unclassified Veillonellaceae.
(TIFF)
Figure S28Gradients ofPropionibacterium,Staphylococcusand
Corynebacteriumabundances in retroauricular crease samples.
HMP samples are shown in a principal coordinates analysis of
unweighted UniFrac distances. Samples are colored according to
(A) putative cluster membership and by their abundances (0–1, see
legend inserts) of (B)Propionibacterium, (C)Staphylococcusand (D)
Corynebacterium.
(TIFF)
Figure S29Gradients ofPropionibacterium,Staphylococcusand
Parabacteroidesabundances in antecubital fossa samples. HMP
samples are shown in a principal coordinates analysis of
unweighted UniFrac distances. Samples are colored according to
(A) putative cluster membership and by their abundances (0–1, see
legend inserts) of (B)Propionibacterium, (C)Staphylococcusand (D)
Parabacteroides.
(TIFF)
Figure S30Gradients ofCorynebacterium,Staphylococcusand
Propionibacteriumabundances in anterior nares samples. HMP
samples are shown in a principal coordinates analysis of
unweighted UniFrac distances. Samples are colored according to
(A) putative cluster membership and by their abundances (0–1, see
legend inserts) of (B)Corynebacterium, (C)Staphylococcusand (D)
Propionibacterium.
(TIFF)
Table S1List of studies used in the data analysis.
(DOCX)
Acknowledgments
Calculations were performed with the assistance of Shane Canon at
NERSC.
Enterotypes across the Human Body
PLOS Computational Biology | www.ploscompbiol.org 15 January 2013 | Volume 9 | Issue 1 | e1002863

Author Contributions
Conceived and designed the experiments: OK DK AG LW NS RK CH
REL. Performed the experiments: OK DK AG LW NS RK CH REL.
Analyzed the data: OK DK AG LW NS RK CH REL. Contributed
reagents/materials/analysis tools: OK DK AG LW NS RK CH REL.
Wrote the paper: OK DK AG NS RK CH REL.
References
1. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, et al. (2010) A human gut
microbial gene catalogue established by metagenomic sequencing. Nature 464:
59–65.
2. The Human Microbiome Project Consortium (2012) Structure, function and
diversity of the healthy human microbiome. Nature 486: 207–214.
3. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, et al. (2011)
Enterotypes of the human gut microbiome. Nature 473: 174–180.
4. The Human Microbiome Project Consortium (2012) A framework for human
microbiome research. Nature 486: 215–221.
5. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, et al. (2010)
QIIME allows analysis of high-throughput community sequencing data. Nat
Methods 7: 335–336.
6. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, et al. (2010)
Impact of diet in shaping gut microbiota revealed by a comparative study in
children from Europe and rural Africa. Proc Natl Acad Sci U S A 107: 14691–
14696.
7. Dethlefsen L, Relman DA (2011) Incomplete recovery and individualized
responses of the human distal gut microbiota to repeated antibiotic perturbation.
Proc Natl Acad Sci U S A 108 Suppl 1: 4554–4561.
8. McDonald D, Price MN, Goodrich J, Nawrocki EP, Desantis TZ, et al. (2011)
An improved Greengenes taxonomy with explicit ranks for ecological and
evolutionary analyses of bacteria and archaea. ISME J 6: 610–618.
9. Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for
comparing microbial communities. Appl Environ Microbiol 71: 8228–8235.
10. Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, et al. (2012)
Metagenomic microbial community profiling using unique clade-specific marker
genes. Nat Methods 9: 811–814.
11. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, et al. (2011) vegan:
Community ecology package. R package version 2.0-0. http://cran.r-project.
org/web/packages/vegan/index.html.
12. Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K (2012) cluster:
Cluster analysis basics and extensions. R package version 1.14.2 ed.
13. Tibshirani R, Walther G (2005) Cluster validation by prediction strength.
J Comput Graph Stat 14: 511–528.
14. Rousseeuw PJ (1987) Silhouettes - a Graphical aid to the interpretation and
validation of cluster-analysis. J Comput Appl Math 20: 53–65.
15. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, et al. (2011) Linking long-
term dietary patterns with gut microbial enterotypes. Science 334: 105–108.
16. Kaufman L, Rousseeuw PJ (1990) Finding groups in data : an introduction to
cluster analysis. Hoboken, N.J.: Wiley. xiv, 342.
17. Duong T (2011) ks: Kernel smoothing.R package version 1.8.2. http://cran.r-
project.org/web/packages/ks/index.html.
18. Hennig C (2010) fpc: Flexible procedures for clustering. http://cran.r-project.
org/web/packages/fpc/index.html.
19. Hennig C, Liao TF (2010) Comparing latent class and dissimilarity based
clustering for mixed type variables with application to social stratification.
Research Report No. 308. Department of Statistical Science, University College
London.
20. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, et al. (2009)
Bacterial community variation in human body habitats across space and time.
Science 326: 1694–1697.
21. Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, et al.
(2010) Delivery mode shapes the acquisition and structure of the initial
microbiota across multiple body habitats in newborns. Proc Natl Acad Sci U S A
107: 11971–11975.
22. Koenig JE, Spor A, Scalfone N, Fricker AD, Stombaugh J, et al. (2011)
Succession of microbial consortia in the developing infant gut microbiome. Proc
Natl Acad Sci U S A 108 Suppl 1: 4578–4585.
23. Milligan GW, Cooper MC (1985) An examination of procedures for
determining the number of clusters in a data set. Psychometrika 50: 159–179.
24. Jain R, Koronios A (2008) Cluster validating techniques in the presence of
duplicates. In: Jain LC, Sato-Ilic M, Virvou M, Tsihrintzis GA, Balas VE. et al.,
editors. Computational Intelligence Paradigms. Springer Berlin Heidelberg.
25. Claesson MJ, Jeffery IB, Conde S, Power SE, O’Connor EM, et al. (2012) Gut
microbiota composition correlates with diet and health in the elderly. Nature
488: 178–184.
26. Ravel J, Gajer P, Abdo Z, Schneider GM, Koenig SS, et al. (2011) Vaginal
microbiome of reproductive-age women. Proc Natl Acad Sci U S A 108 Suppl 1:
4680–4687.
27. Kuczynski J, Lauber CL, Walters WA, Parfrey LW, Clemente JC, et al. (2012)
Experimental and analytical tools for studying the human microbiome. Nat Rev
Genet 13: 47–58.
28. Kumar PS, Brooker MR, Dowd SE, Camerlengo T (2011) Target region
selection is a critical determinant of community fingerprints generated by 16S
pyrosequencing. PloS ONE 6: e20956.
29. Caporaso JG, Lauber CL, Costello EK, Berg-Lyons D, Gonzalez A, et al. (2011)
Moving pictures of the human microbiome. Genome Biol 12: R50.
30. Ley RE, Turnbaugh PJ, Klein S, Gordon JI (2006) Microbial ecology: human
gut microbes associated with obesity. Nature 444: 1022–1023.
31. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, et al.
(2009) A core gut microbiome in obese and lean twins. Nature 457: 480–484.
32. Costello EK, Gordon JI, Secor SM, Knight R (2010) Postprandial remodeling of
the gut microbiota in Burmese pythons. ISME J 4: 1375–1385.
33. Crawford P, Crowley J, Sambandam N, Muegge B, Costello E, et al. (2009)
Regulation of myocardial ketone body metabolism by the gut microbiota during
nutrient deprivation. Proc Natl Acad Sci U S A 106: 11276–11281.
34. Jumpertz R, Le DS, Turnbaugh PJ, Trinidad C, Bogardus C, et al. (2011)
Energy-balance studies reveal associations between gut microbes, caloric load,
and nutrient absorption in humans. Am J Clin Nutr 94: 58–65.
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