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ARTICLE
Visual analysis of mass cytometry data by
hierarchical stochastic neighbour embedding
reveals rare cell types
Vincent van Unen 1, Thomas Höllt2,3, Nicola Pezzotti2,NaLi
1, Marcel J.T. Reinders 4, Elmar Eisemann2,
Frits Koning1, Anna Vilanova2& Boudewijn P.F. Lelieveldt4,5
Mass cytometry allows high-resolution dissection of the cellular composition of the immune
system. However, the high-dimensionality, large size, and non-linear structure of the
data poses considerable challenges for the data analysis. In particular, dimensionality
reduction-based techniques like t-SNE offer single-cell resolution but are limited in the
number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor
Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a
hierarchy of non-linear similarities that can be interactively explored with a stepwise increase
in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders
and three other available mass cytometry data sets. We find that HSNE efficiently replicates
previous observations and identifies rare cell populations that were previously missed due to
downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a
feature that makes it highly suitable for the analysis of massive high-dimensional data sets.
DOI: 10.1038/s41467-017-01689-9 OPEN
1Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands. 2Computer
Graphics and Visualization Group, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. 3Computational Biology Center, Leiden
University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands. 4Pattern Recognition and Bioinformatics Group, Delft University of Technology,
Mekelweg 4, 2628 CD Delft, The Netherlands. 5Division of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2,
2333 ZA Leiden, The Netherlands. Vincent van Unen, Thomas Höllt and Nicola Pezzotti contributed equally to this work. Frits Koning, Anna Vilanova and
Boudewijn P.F. Lelieveldt jointly supervised this work. Correspondence and requests for materials should be addressed to V.v.U. (email: V.van_unen@lumc.nl)
or to B.P.F.L. (email: B.P.F.Lelieveldt@lumc.nl)
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Mass cytometry (cytometry by time-of-flight; CyTOF)
allows the simultaneous analysis of multiple cellular
markers (>30) present on biological samples consisting
of millions of cells. Computational tools for the analysis of such
data sets can be divided into clustering-based and dimensionality
reduction-based techniques1, each having distinctive advantages
and disadvantages. The clustering-based techniques, including
SPADE2, FlowMaps3, Phenograph4, VorteX5and Scaffold maps6,
allow the analysis of data sets consisting of millions of cells but
only provide aggregate information on generated cell clusters at
the expense of local data structure (i.e., single-cell resolution).
Dimensionality reduction-based techniques, such as PCA7,t-
SNE8(implemented in viSNE9), and Diffusion maps10, do allow
analysis at the single-cell level. However, the linear nature of PCA
renders it unsuitable to dissect the non-linear relationships in the
mass cytometry data, while the non-linear methods (t-SNE8and
Diffusion maps10) do retain local data structure, but are limited
by the number of cells that can be analyzed. This limit is imposed
by a computational burden but, more importantly, by local
neighborhoods becoming too crowded in the high-dimensional
space, resulting in overplotting and presenting misleading infor-
mation in the visualization. In cytometry studies, this poses a
problem, as a significant number of cells needs to be removed by
random downsampling to make dimensionality reduction com-
putationally feasible and reliable. Future increases in acquisition
rate and dimensionality in mass- and flow cytometry are expected
to amplify this problem significantly11,12.
Here we adapted Hierarchical stochastic neighbor embedding
(HSNE)13 that was recently introduced for the analysis of
hyperspectral satellite imaging data to the analysis of mass
Marker b
Marker a
Marker c
a
HSNE
(2 levels)
HSNE 1 HSNE 1
HSNE 2
HSNE 2
AoI
(# Events)
b
Overview level Data level
Construction Exploration
c
1
2
Embedding
color: marker a
Density
Heatmap
Color: marker c
Color: marker b
23
1
1
2
3
4
HSNE 1
HSNE 2
Cell
Landmark
AoI
1
2
23
1
123
a
b
c
a
b
c
12
1
2
3
4
1234
a
b
c
AoI Density
Expression
Events / area of influence (AoI)
Hierarchy construction (high-dimensional space)
Neighborhood graph Embeddings and clustering
Hierarchy exploration (two-dimensional space)
Intermediate levels
(arbitrary number)Overview level Data level
Fig. 1 Schematic overview of Cytosplore+HSNE for exploring the mass cytometry data. By creating a multi-level hierarchy of an illustrative 3D data set (a),
we achieve a clear separation of different cell groups in an overview embedding (left panel b) that conserves non-linear relationships (i.e., follows the
distance indicated by the dashed line in a, instead of the grey arrow) and more detail within the separate groups on the data level (right panel b).
cConstruction and exploration of the hierarchy. The hierarchy is constructed starting with the data level (left two columns). On the basis of the
high-dimensional expression patterns of the cells, a weighted kNN graph is constructed, which is used to find representative cells used as landmarks in the
next coarser level. By administering the area of influence (AoI) of the landmarks, cells/landmarks can be aggregated without losing the global structure of
the underlying data or creating shortcuts. The exploration of the hierarchy is shown in the two rightmost columns. At the bottom, we see the overview level
(in this example the 3rd level in the hierarchy), which shows that a group of landmarks has low expression in marker c (bottom-right panel). Selecting this
group of landmarks for further exploration results in a look-up of the landmarks in the preceding level (neighborhood graph, intermediate level) that are in
the AoI, with which a new embedding can be created at the 2nd level of the hierarchy (middle-right panel). Marker b shows a strong separation between
the upper and lower landmarks at this level. Zooming-in on the landmarks with low expression of marker b reveals further separation in marker a at the
lowest level, the full data level (top-right panel)
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cytometry data sets to visually explore millions of cells while
avoiding downsampling. HSNE builds a hierarchical representa-
tion of the complete data that preserves the non-linear high-
dimensional relationships between cells. We implemented HSNE
in an integrated single-cell analysis framework called Cytosplore
+HSNE. This framework allows interactive exploration of the
hierarchy by a set of embeddings, two-dimensional scatter plots
where cells are positioned based on the similarity of all marker
expressions simultaneously, and used for subsequent analysis
such as clustering of cells at different levels of the hierarchy. We
found that Cytosplore+HSNE replicates the previously identified
hierarchy in the immune-system-wide single-cell data4,5,14, i.e.,
we can immediately identify major lineages at the highest over-
view level, while acquiring more information by dissecting the
immune system at the deeper levels of the hierarchy on demand.
Additionally, Cytosplore+HSNE does so in a fraction of the
time required by other analysis tools. Furthermore, we identified
rare cell populations specifically associating to diseases in both
the innate and adaptive immune compartments that were pre-
viously missed due to downsampling. We highlight scalability and
generalizability of Cytosplore+HSNE using three other data sets,
consisting of up to 15 million cells. Thus, Cytosplore+HSNE
combines the scalability of clustering-based methods with the
local single-cell detail preservation of non-linear dimensionality
reduction-based methods. Finally, Cytosplore+HSNE is not only
applicable to mass cytometry data sets, but can be used for
the other high-dimensional data like single-cell transcriptomic
data sets.
HSNE clusters
Annotated subsets
(% of HSNE clusters)
0
20
40
60
80
100
Overview level
All cells
Visualization: annotated
subsets
HSNE generated using 5.2×106 cells
HSNE generated
using 1.1×106 cells
Level 2
Downsampled +
discarded
Level 3
ab
d
c
Downsampled
Discarded
Annotated subsets
0 142
×106 cells
3.0
1.1
1.1
Density
HighLow
HSNE 1
HSNE 2
Fig. 2 Gain of information by analyzing the mass cytometry data at full resolution with Cytosplore+HSNE .aPie chart showing cellular composition of
the mass cytometry data set. Color represents the subsets (N=142), as identified in our previous study14. Black represents the cells discarded by
stochastic downsampling and grey represents the cells discarded by ACCENSE clustering. bEmbeddings of the 1.1 million cells annotated in ref 14 showing
the top three levels of the HSNE-hierarchy (five levels in total). Color represents annotations as in a. Size of the landmarks is proportional to the number
of cells in the AoI that each landmark represents. Bottom map shows density features depicting the local probability density of cells for the level 3
embedding, where black dots indicate the centroids of identified cluster partitions using GMS clustering. cEmbeddings of all 5.2 million cells, again showing
only the top three levels of the hierarchy (five levels in total). Colors as in a. Right panels visualize landmarks representing cells discarded by
stochastic downsampling (black) and the cells discarded by ACCENSE (grey). Bottom map shows density features for the level 3 embedding as
described in (b). dFrequency of annotated cells for 145 clusters identified by Cytosplore+HSNE at the third hierarchical level using GMS clustering in c.
Color coding as in a
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Results
Hierarchical exploration of massive single-cell data. For a given
high-dimensional data set such as the three-dimensional illus-
trative example in Fig. 1a, HSNE13 builds a hierarchy of local
neighborhoods in this high-dimensional space, starting with the
raw data that, subsequently, is aggregated at more abstract
hierarchical levels. The hierarchy is then explored in reverse order,
by embedding the neighborhoods using the similarity-based
embedding technique, Barnes–Hut (BH)-SNE15. To allow for
more detail and faster computation, each level can be partitioned
in part or completely, by manual gating or unsupervised cluster-
ing, and partitions are embedded separately on the next, more
ILC
and
ILC-like cells
cCD127CD7 CD45RA CD56
CD38 NKp46
CD161
Level 3
5.0×105 cells (9.6 %)
Density
HighLow
e
g
CD127CD7 CD45RA CD56
CRTH2 c-KIT
CD27
Data level
6.0×104 cells (1.2 %)
f
Cluster partitions
Density
Blood/intestine Clinical features Sampling
18
Cell frequencies (fraction of cluster)
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1
CD19
CCR6
c-KIT
CD11b
CD4
CD8a
CD7
CD25
CD123
TCRγδ
CD45
CRTH2
CD122
CCR7
CD14
CD11c
CD161
CD127
CD8b
CD27
IL-15Ra
CD45RA
CD3
CD28
CD38
NKp46
PD-1
CD56
16
11
15
12
13
1
19
9
5
4
10
17
14
6
8
7
2
3
16
11
15
12
13
1
19
9
5
4
10
17
18
14
6
8
7
2
3
16 15 4
17
11
10
8
18
14
19
13 3
512
7
9
6
2
1
CD4+ T cells
CD8+ T cells
TCRγδ
Innate
lymphocytes
B cells
Myeloid1
Myeloid2
CD7 CD3 CD4
Overview level
5.2×106 cells (100 %)
ab
CD8a
TCRγδ
CD19
CD11c
Marker expression
50
HSNE 1
HSNE 2
Fig. 5
d
Select and zoom-In
Cluster
unique to RCDII
Tissue
Blood
RCDII
EATLII
Crohn
CeD
Ctrl
Blood
Intestine
Ctrl
CeD
Crohn
EATLII
RCDII
Subset
Discarded
Downsampled
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detailed level (compare Fig. 1b). HSNE works particularly well for
the analysis of the mass cytometry data because the local neigh-
borhood information of the data level is propagated through the
complete hierarchy. Groups of cells that are close in the Euclidian
sense (Fig. 1a, grey arrow), but not on the non-linear manifold
(Fig. 1a, dashed black line), are well separated even at higher
aggregation levels (Fig. 1b). The power of HSNE lies in its scal-
ability to tens of millions of cells, while the possibility to con-
tinuously explore the hierarchy allows the identification of rare
cell populations at the more detailed levels. Next follows a general
description of how the hierarchy is built and explored through
embeddings. More details can be found in the Methods section.
The left panels of Fig. 1c give an overview of the HSNE-hierarchy
construction. We show the hierarchy from the fine-grained data
level to an overview level from the top to bottom panels. The
number of levels is defined by the user and depends mostly on the
input-data size. While the data aggregation is completely data-
driven, for a typical mass cytometry data set, every additional level
reduces the number of landmarks by roughly one order of
magnitude. Therefore, we recommend to use log10(N/100) levels,
with N being the number of cells: this generally results in at most
few thousands of landmarks at the highest level of the hierarchy.
The foundation of the hierarchy is constructed using the original
input data. Each dot represents a single cell (Fig. 1c, data level).
Similarities between cells on the data level are defined by building
an approximated, weighted k-nearest neighbor (kNN) graph16
using the Euclidian distances based on the complete marker
expression (Fig. 1c, top-center panel). The weights of this graph can
directly be used as input to embed the data into a two-dimensional
space (Fig. 1c, top-right panel). With the BH-SNE the two-
dimensional embedding is generated such that the layout of the
points indicates similarities between the cells in the high-
dimensional space according to the neighborhood graph.
To aggregate the data into the next level (Fig. 1c, intermediate
levels), we identify representative cells to use as landmarks (Fig. 1c,
white circles). For that, the weighted kNN graph is interpreted as a
Finite Markov Chain and the most influential (i.e., best-
connected) nodes are chosen as landmarks, using a Monte Carlo
process. The landmarks are then embedded into a two-
dimensional space based on their similarities. However, simply
repeating the kNN construction with Euclidian distances for the
selected landmarks in the high-dimensional space would even-
tually eliminate non-linear structures by creating undesired
“shortcuts”in the graph (a problem reported by Setty et al.17 in
a different setting). Instead, we define the area of influence (AoI)
of each landmark, indicated by the grey hulls (Fig. 1c, left panels),
as the cells that are well-represented by the landmark according to
the kNN graph. Different landmarks can have overlapping regions
of locally-similar cells. Therefore, we define the similarity of two
landmarks as the overlap of their respective AoIs. Furthermore, we
construct a neighborhood graph, based on these similarities. Here,
two nodes are connected if they have overlapping AoIs. The
strength of the connection is defined by the number of data points
within the overlapping region. This graph replaces the kNN graph
as input for levels subsequent to the data level. Hereby, we
effectively maintain the non-linear structure of the data to the top
of the hierarchy and avoid shortcuts (Fig. 1c, bottom panels). We
show that the preservation of non-linear neighborhoods by HSNE
indeed conserves structure that is otherwise lost by random
downsampling (Supplementary Note 1. Cytosplore+HSNE is
reproducible and robust. and Supplementary Fig. 1).
The data exploration in Cytosplore+HSNE starts with the
visualization of the embedding at the highest level, the overview
level (Fig. 1c, bottom-right panel). Similar to other embedding
techniques for visualizing the single-cell data4,9, the layout of
the landmarks indicates similarity in the high-dimensional
space according to the level’s neighborhood graph. Color is
used to represent additional traits, such as marker expressions.
The landmark size reflects its AoI. While it is possible to
continuously select all landmarks and compute a complete
embedding of the next, more detailed level, this strategy would
eventually embed all the data and suffer from the same scalability
problems as a t-SNE embedding, i.e., overcrowding (Supplemen-
tary Note 2. Millions of cells cause performance issues and
overcrowding in t-SNE. and Supplementary Fig. 2) and slow
performance. Instead, we envision that the user selects a group of
landmarks, by manual gating based on visual cues such as patterns
found in marker expression, or by performing unsupervised
Gaussian mean shift (GMS) clustering18 of the landmarks based
on the density representation of the embedding (Fig. 1c, right
panels). Then, the user can zoom into this selection by means of a
more detailed embedding. This means that, all landmarks/cells in
the combined AoI on the preceding level are retrieved from the
neighborhood graph (Fig. 1c, blue encirclements), embedded, and
visualized in a new view. Moreover, interactively linked heatmap
visualizations of clusters (Fig. 1c, right panels) and descriptive
statistics of markers within a selection can be used to guide
the exploration. For example, these tools allow to inspect the
heterogeneity of cells within individual clusters, including the cells
associated to individual landmarks. Importantly, all of the
described tools are available at every level of the hierarchy and
linked interactively. Selections in the embedding and heatmap at
one level of the hierarchy can thus be highlighted in the
embeddings of other levels (Supplementary Fig. 3). All these
aspects are further demonstrated using a typical exploration
workflow with Cytosplore+HSNE in the Supplementary Movie 1.
With this strategy, tens of millions of cells can be explored,
providing both global visualizations up to single-cell resolution
visualizations, while preserving non-linear relationships between
landmarks/cells at all levels of the hierarchy.
HSNE eliminates the need for downsampling. In a previous
study14, a mass cytometry data set on 5.2 million cells derived
from intestinal biopsies and paired blood samples was analyzed
using a SPADE-t-SNE-ACCENSE pipeline. Due to t-SNE
Fig. 3 Analysis of the CD7+CD3−innate lymphocyte compartment in inflammatory intestinal diseases. aFirst HSNE level embedding of 5.2 million cells.
Color represents arcsin5-transformed marker expression as indicated. Size of the landmarks represents AoI. Blue encirclement indicates selectionof
landmarks representing CD7+CD3−innate lymphocytes and CD4+T cells further discussed in Fig. 5.bThe major immune lineages, annotated on the basis of
lineage marker expression. cThird HSNE level embedding of the CD7+CD3−innate lymphocytes (5.0 × 105cells). Color represents arcsin5-transformed
marker expression in top panels, and tissue-origin and clinical features in bottom panels. Blue encirclement indicates selection of landmarks representing
CD127+ILC and ILC-like cells. dThird HSNE level embedding shows density features depicting the local probability density of cells, where black dots indicate
the centroids of identified cluster partitions using GMS clustering. eEmbedding of the CD127+ILC and ILC-like cells (6.0 × 104cells) at single-cell resolution.
Arrows indicate ILC1 (blue), ILC2 (orange) and ILC3 (green). Bottom-right panel shows corresponding cluster partitions using GMS clustering based on
density features (top-right panel). fA heatmap summary of median expression values (same color coding as for the embeddings) of cell markers expressed
by CD127 + ILC and ILC-like clusters identified in band hierarchical clustering thereof. gComposition of cells for each cluster is represented graphically by a
horizontal bar in which segment lengths represent the proportion of cells with: (left) tissue-of-origin, (middle) disease status and (right) sampling status
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limitations, the data set had to be downsampled by 57.7%
(Fig. 2a), where it was decided to equal the number of cells from
blood and intestinal samples for a balanced comparison, which led
to the exclusion of more cells from the blood samples. Moreover,
ACCENSE clustered only 50% of the t-SNE-embedded data into
subsets (Fig. 2a). Together, this excluded 78.8% of the cells from
the analysis. The remaining 1.1 million cells were annotated into
142 phenotypically distinct immune subsets14 (Fig. 2a).
To determine whether Cytosplore+HSNE could identify similar
subsets, we embedded the 1.1 million annotated cells (Fig. 2b).
Computation time was in the order of minutes and the analysis
was finished within an hour, compared to 8 weeks of computation
in the original study. Color coding shows the grouping of subsets
at all hierarchical levels. GMS clustering at the third level
embedding (Fig. 2b, bottom panel) reveals that 75.5% of cells
were assigned to a single subset by both methods (Supplementary
Fig. 4). Hence, to reach similar results it was not necessary to
explore the data at lower (more detailed) levels.
Next, we utilized Cytosplore+HSNE to analyze the complete
dataset on 5.2 million cells, thus including the cells that were
discarded in the SPADE-t-SNE-ACCENSE pipeline. The embed-
dings show by color coding that subsets of the same immune
lineage clustered at all three levels (Fig. 2c). More interestingly, the
cells removed during downsampling (shown in black) and cells
ignored during the ACCENSE clustering (shown in grey) were
positioned throughout the entire map (Fig. 2c). We selected 145
clusters using GMS clustering at the third level and observed that
the identified clusters contained variable numbers of downsampled
and non-classified cells (Fig. 2d). These findings indicate that both
the non-uniform downsampling and the cell losses during the
ACCENSE clustering introduce a potential bias in observed
heterogeneity in the immune system. Cytosplore+HSNE overcomes
this problem as it analyzes all cells and does so efficiently.
HSNE identifies rare subsets in the ILC compartment.We
illustrate an exploration workflow with Cytosplore+HSNE using
the data set of 5.2 million cells14 (Fig. 3). At the overview level,
4090 landmarks depict the general composition of the immune
system (Fig. 3a) and color coding is applied to reveal CD-marker
expression patterns on the basis of which the major immune
lineages are identified (Fig. 3b). Next the CD7+CD3−cell clusters
were selected as indicated and a new higher resolution embedding
was generated at level 3 of the hierarchy (Fig. 3c). Here, coloring
of the landmarks based on marker expression (Fig. 3c, top panels)
and a density plot of the embedding is shown (Fig. 3d) alongside
the clinical features of the subjects from which the samples
were obtained and the tissue-origin of the landmarks (Fig. 3c,
bottom panels). This reveals a cluster of cells abundantly
present in the intestine of patients with refractory celiac disease
(RCDII). In addition, a large cluster of CD45RA+CD56+NK cells
and three distinct innate lymphoid cell (ILC) clusters with a
characteristic lineage−CD7+CD161+CD127+marker expression
profile19,20 are visualized. Strikingly, a distinct population of
CD7+CD127−CD45RA−and partly CD56+cells is found in
between the NK, RCDII and ILC cell clusters.
To uncover the phenotypes of these ILC-related clusters, we
next embedded the ILC and ILC-like clusters (Fig. 3c, selection) at
the full single-cell data level (59,775 cells; 1.2% of total) (Fig. 3e).
The marker expression overlays revealed that the majority of cells
are CD7+and displayed variable expression levels for CD127,
CD45RA, and CD56 (Fig. 3e). In addition, and in line with
previous reports21,22, (co-)expression of CD127 with CD27,
CRTH2, and c-KIT revealed the phenotypes corresponding to
helper-like ILC type 1, 2 and 3, respectively (indicated by arrows
in Fig. 3e). Moreover, by visualizing the tissue-origin in the
Cytosplore+HSNE embedding the tissue-specific location of ILC
and ILC-related phenotypes became evident (Fig. 3e).
Subset Phenotype Annotation
16 CD127+CD161+CD25+CD122–CRTH2+ILC2
15 CD127+CD161+CD25+CD122–CRTH2–ILC2-like
4 CD56+NKp46+CD127–CD161–c-KIT–NK-like
17 CD56+NKp46+CD127+CD161–c-KIT–ILC1-like
9 CD56+NKp46+CD127+CD161–c-KIT–ILC1-like
11 CD56+NKp46+CD127+CD161–c-KIT–ILC1-like
10 CD56+NKp46+CD127–CD161–c-KIT–NK-like
1 CD7–CD127+CD161+c-KIT+ILC3-like
5 CD7+CD127+CD161+c-KIT+ILC3
12 CD56+CD127+CD161+c-KIT–CD27–ILC1-like
19 CD56–CD127–NKp46–CD161dim Lin- cells
13 CD56–CD127–NKp46–CD161dim Lin- cells
18 CD56–CD127–NKp46+CD161–Lin- cells
14 CD56–CD127–NKp46+CD161–Lin- cells
6 CD56–CD127–NKp46+CD161+Lin- cells
8 CD56–CD127–NKp46+CD161+Lin- cells
7 CD56+CD127–CD45RA–CD161–NK-like
2 CD56+CD127–CD45RA–CD161+NK-like
3 CD56+CD127–CD45RA–CD161+NK-like
Fig. 4 CD127+ILC and ILC-like subsets identified by Cytosplore+HSNE. Table showing cluster number, distinguishing phenotypic marker expression profiles
and biological annotation for the clusters identified in Fig. 3e. Black color indicates clusters described in previous reports and red color additional unknown
clusters. Hierarchical clustering of clusters based on marker expression profile shown in the heatmap depicted in Fig. 3f
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Next, we performed GMS clustering on the full data level
embedding, which resulted in 19 phenotypically distinct clusters
(Fig. 3e, right plots) based on marker expression profiles (Fig. 3f).
The cell surface phenotypes of 8 out of the 19 clusters (Fig. 3f)
matched previously described21 biological annotations (Fig. 4,
black annotations) including the CRTH2+ILC2 (cluster 16), c-
KIT+ILC3 (cluster 5) and CD56−CD127−lineage−IELs (cluster
19, 13, 18, 14, 6, and 8), the latter representing innate type of
lymphocytes with dual T-cell precursor and NK/ILC traits23–25.
Remarkably, the remaining 11 clusters strongly resembled distinct
ILC types, but did not fulfil the complete phenotypic require-
ments according to established nomenclature21 (Fig. 4, red
annotations). For example, cluster 15 is highly similar to ILC2
(cluster 16) based on the expression of CD7, CD127, CD161,
and CD25, but lacks the ILC2-defining marker CRTH2. Also,
clusters 17, 9 and 11 bear close resemblance to ILC1 based
on CD7+CD127+c-KIT−marker expression profile, but lack the
ILC-defining CD161 marker. Finally, cluster 1 is very similar to
ILC3 (cluster 5) based on CD127, CD161 and c-KIT positivity,
but lacks the lymphoid marker CD7. Interestingly, the ILC3
(cluster 5) and ILC3-like (cluster 1) populations resided mainly in
intestinal biopsies of patient with Crohn’s disease (Fig. 3f) and
may be related. Cluster 4 was mainly present in peripheral blood
of patients with RCDII, suggesting a possible association with this
pre-malignant disease state. Importantly, three clusters (4, 17, and
19) (Fig. 3f) were essentially missed in our previous study14 due
to the downsampling. Finally, all identified cell clusters consist to
a variable extent of cells that were downsampled in the original
analysis (Fig. 3g). Thus, the analysis of the full data set provides
increased detail and confidence in establishing the phenotypes of
these low abundance innate cell subsets.
HSNE identifies rare CD4+T-cell subsets in blood. Next, we
selected the CD4+T-cell lineage (Fig. 3a) and show the distribu-
tion of the landmarks at the third level, revealing several clusters
within the CD4+T-cell compartment (Fig. 5a), including a small
CD28−CD4+T-cell memory population (25,398 cells; 0.5% of
total), most likely representing terminally differentiated cells26.
Subsequent analysis at the single-cell level (Fig. 5b) identified a
CD56+population within the CD28−CD4+T cells that is enriched
in blood of patients with Crohn’s disease (Fig. 5b, bottom panels,
dashed black circle), as well as a CD56−population of CD28−CD4
+T cells (Fig. 5b, bottom panels, dashed yellow circle) present in
blood samples of both patients and controls. Importantly, this
latter cell population was not identified in our previous publica-
tion due to the non-uniform downsampling of cells (Fig. 5b).
Together, these findings emphasize that Cytosplore+HSNE is
highly efficient in unbiased analysis of both abundant and rare cell
populations in health and disease by permitting full single-cell
CD28–
memory
a
b
Marker expression
50
CCR7CD45RA CD28 CD27
CD127 CD38
CD161
Data level
2.5×104 cells (0.5 %)
CCR7 CD27
CD45RA CD127 CD28 CD7
CD56
Density
Density
HighLow
Level 3
1.9×106 cells (36.9 %)
RCDII
EATLII
Crohn
CeD
Ctrl
Downsampled
Discarded
Subset
Intestine
Blood
Fig. 5 Analysis of the CD4+T-cell compartment in inflammatory intestinal diseases. aThird HSNE level embedding of the CD4+T cells (1.4 × 106cells,
selected in Fig. 3). Color and size of landmarks as described in Fig. 3. Right panel shows density features for the level 3 embedding. Blue encirclement
indicates selection of landmarks representing CD28−CD4+T cells. bEmbedding of the CD28−CD4+T cells (2.6 × 104cells) at single-cell resolution.
Bottom-left panel shows yellow and black dashed encirclements based on CD56−and CD56+expression, respectively. Three bottom-right panels show
cells colored according to: (left) from subjects with different disease status (CeD, Crohn, EATLII, RCDII, and controls), (middle) sampling status (annotated
subset, discarded by ACCENSE and downsampled) and (right) tissue-of-origin (blood and intestine)
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resolution. It enables the simultaneous identification and visua-
lization of known cell subsets and provides evidence for additional
heterogeneity in the immune system, as it reveals the presence of
cell clusters that were missed in a previous analysis due to
downsampling of the input data. These currently unspecified cell
clusters might represent intermediate stages of differentiation or
novel rare cell types with presently unknown function.
HSNE is robust and outperforms current single-cell methods.
While the exploration of the hierarchy requires analysis at multiple
levels, the workflow is robust and reproducible as shown in Sup-
plementary Fig. 5. In this exemplary analysis, we obtained the same
Cytosplore+HSNE clusters at the single-cell level upon reconstructing
the hierarchy and embeddings in a matter of minutes (Methods
section). In addition, we tested the Cytosplore+HSNE applicability to
three different public mass cytometry data sets. First, we analyzed a
well-characterized bone marrow data set27 containing 81,747 cells
as a benchmark case (Supplementary Fig. 6) and demonstrated that
the landmarks in the overview level (2632; 3.2% of total) that were
selected by the HSNE algorithm were distributed across almost all
of the manually gated cell types (Supplementary Fig. 6a), indicating
that the global data heterogeneity was accurately preserved. Also,
GMS clustering resulted in HSNE clusters that were phenotypically
similar to the manually gated cell types and displayed additional
diversity within those subsets (Supplementary Fig. 6b). However, as
the power of Cytosplore+HSNE lies in its scalability to data sets
exceeding millions of cells, we also tested the versatility of Cytos-
plore+HSNE by comparing it to other state-of-the-art scalable single-
cell analysis methods and accompanying large data sets (Supple-
mentary Note 3. Cytosplore+HSNE offers advantages over current
scalable single-cell analysis methods, Supplementary Figs. 7and 8).
Here Cytosplore+HSNE computed the analyses of the VorteX data
set5containing 0.8 million cells in 4 min compared to 22 h, using
the publicly available VorteX implementation on the same com-
puter. Similarly, analysis of the Phenograph data set4containing 15
million cells was computed in 3.5 h compared to 40 h, using the
publicly available Phenograph implementation on the same com-
puter. Both analyses show that Cytosplore+HSNE reproduces the
main findings as presented in the original publications. More
importantly, Cytosplore+HSNE provides the distinct advantage of
visualizing all cells and intracluster heterogeneity at subsequent
levels of detail up to the single-cell level, even for the 15 million of
cell data set, without a need for downsampling. Also, VorteX failed
computing the 5.2 million cell gastrointestinal data set within 3 days
of clustering (regardless of using Euclidian or Angular distance),
where Cytosplore+HSNE accomplished this within 29 min. More-
over, while Phenograph did identify rare clusters that largely con-
sisted of CD56+cells within the CD28−
CD4+memory T cells
(Fig. 5b), these clusters did not accurately correspond to the total
number of CD56+cells, obscuring the association with Crohn’s
disease, further highlighting the advantages of Cytosplore+HSNE
over these other computational tools.
Finally, we investigated whether a density-based downsampling
as implemented for instance by SPADE2, could provide better
results compared to random downsampling. However, solely
applying density-based downsampling does not allow for
quantitative analysis of the resulting sample, as different types
of cells will be reduced by different amounts. To mitigate this
problem, SPADE implements an elaborate pipeline of down-
sampling, clustering and subsequent upsampling to enable for
such a comparison, while this is an inherent part of HSNE.
Therefore, we made a direct comparison between density-based
downsampling used in the SPADE pipeline2and HSNE of the
same 5.2 million cells gastrointestinal data set. On the basis of the
expression of major lineage markers (Fig. 3a), HSNE created six
large clusters (Fig. 3b) in the two-dimensional space at the
overview level where similar landmark cells group closely, laying
out all the cells of one cluster very close to any other cell of the
same cluster, but distant from the cells of the other clusters. The
SPADE analysis on the same data (Supplementary Fig. 9) created
a dendrogram where cells of one cluster are close to cells of other
clusters, while in high-dimensional space, they could be dissimilar
and far apart. Importantly, we compared the ability of the SPADE
analysis to preserve rare cellular subsets with HSNE. Despite
density-based downsampling, several SPADE nodes that were
created displayed a mixture of different phenotypes (under-
clustering) as revealed by the single-cell resolution of a linked t-
SNE analysis that we show for the CD56+CD4+T-cell node as an
example (Supplementary Fig. 9b, node #1), while other SPADE
nodes contained cells with overlapping phenotypes (overcluster-
ing) such as several myeloid cell populations (Supplementary
Fig. 9c, nodes #2–5). In addition, rare subsets such as the CD28−
subpopulations of CD4+memory T cells (Supplementary Fig. 9d)
or the ILC-like clusters (Supplementary Fig. 9e) that we could
identify with HSNE (Figs. 3and 5) were in the resulting SPADE
tree indistinguishable from other CD4+T cells or innate
lymphocytes, respectively (shown by the overlapping distribu-
tions of cells from different nodes); this indicates that SPADE is
less suitable for rare cell analysis. A similar problem was reported
by Amir et. al., where leukemic cells were not separated from
healthy cells in the SPADE tree9. Thus, combining the single-cell
resolution with the enhanced scalability may be critical for the
success of HSNE in preserving rare cells.
Discussion
Mass cytometry data sets generally consist of millions of cells.
Current tools can either extract global information with no
single-cell resolution or provide single-cell resolution but at the
expense of the number of cells that can be analyzed. Conse-
quently, when single-cell resolution is of interest, most current
tools require downsampling of the data sets. However, reducing
the number of included cells in the analysis pipeline may hamper
the identification of rare subsets.
To overcome this problem, we introduce Cytosplore+HSNE.On
the basis of a novel hierarchical embedding of the data (HSNE),
Cytosplore+HSNE enables the analysis of tens of millions of cells
using the whole data in a fraction of the time required by
currently available tools. The power of the hierarchical embed-
ding strategy is that Cytosplore+HSNE provides visualizations
of the data at different levels of resolution, while preserving the
non-linear phenotypic similarities of the single cells at each level.
Cytosplore+HSNE enables the user to interactively select the
groups of data points at each resolution level, either hand-picked
or guided by density-based clustering, to further zoom-in on the
underlying data points in the hierarchy up to the single-cell
resolution. Using a data set of 5.2 million cells, we demonstrate
that Cytosplore+HSNE allows a rapid analysis of the composition
of the cells in the data set that, at all levels of the hierarchy, the
representation of these cells preserve phenotypic relationships,
and that one can zoom-in on rare cell populations that were
missed with other analysis tools. The identification of such rare
immune subsets offers opportunities to determine cellular para-
meters that correlate with disease.
There is an ongoing scientific debate on the validity of clus-
tering in t-SNE maps versus direct clustering on the high-
dimensional space. However, it has been shown that stochastic
neighbor embedding (SNE) preserves and separates clusters in the
high dimensional space28. While clustering the data points on
highly non-linear manifolds is possible with complex models, we
argue that the presented approach simplifies clustering
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considerably. We show that HSNE efficiently unfolds the non-
linearity in the high-dimensional data, as other SNE approaches
do and therefore simpler clustering methods based on locality in
the map suffice to partition the data faithfully (e.g., the density-
based GMS clustering, implemented in Cytosplore+HSNE). Espe-
cially when combined with an interactive quality control
mechanism to visually inspect residual variance within each
cluster, the kernel size can be selected such that within-cluster
variance is minimized, and thereby supports the validity of the
cluster with respect to potential underclustering. This is indeed
confirmed by comparisons to other scalable tools (i.e., Phenograph
and VorteX), showing that Cytosplore+HSNE provides a superior
discriminatory ability to identify and visualize rare phenotypically
distinct cell clusters in large data sets in a very short time span.
However, depending on user preference, Cytosplore+HSNE can be
used in conjunction with such direct clustering approaches. This
allows the user to identify additional heterogeneity that is poten-
tially missed by direct clustering, and provides the tools for an
informed merging and splitting of clusters as the user deems
appropriate. The recent application of mass cytometry and other
high-dimensional single-cell analysis techniques has greatly
increased the number of phenotypically distinct cell clusters
within the immune system. This raises obvious questions about
the true distinctiveness and function of such cell clusters in health
and disease, an issue that is beyond the scope of the present study
but needs to be addressed in future studies.
In conclusion, Cytosplore+HSNE allows an interactive and fast
analysis of large high-dimensional mass cytometry data sets from a
global overview to the single-cell level and is coupled to patient-
specificfeatures.Thismayprovidecrucialinformationforthe
identification of disease-associated changes in the adaptive and
innate immune system which may aid in the development of dis-
ease- and patient-specific treatment protocols. Finally, Cytosplore
+HSNE applicability goes beyond analyzing mass cytometry data sets
as it is able to analyze any high-dimensional single-cell data set.
Methods
HSNE algorithm. HSNE builds a hierarchy of local and non-linear similarities of
high-dimensional data points13, where landmarks on a coarser level of the hier-
archy represent a set of similar points or landmarks of the preceding more detailed
level. To represent the non-linear structures of the data, the similarity of these
landmarks is not described by Euclidian distance, but by the concept of AoI on
landmarks of the preceding level. The similarities described in every level of the
hierarchy are then used as input for an adapted version of the similarity-based
embedding technique BH-SNE15 for visualization.
The algorithm works as follows: First, a weighted k-nearest neighbor (kNN)
graph is computed from the raw input data. For optimal performance and
scalability, the neighborhoods are approximated as described in ref. 16. The weight
of the link between the two data points in the kNN graph describes the similarity of
the connected data points.
In the subsequent steps, the hierarchy is built based on the similarities of the data
level. To this extent, a number of random walks of predefined length is carried out
starting from every node in the kNN graph, using the similarities as probability for
the next jump; similar nodes to the current node are more likely to be the target of
the next jump. Nodes in the graph that are reached more often are considered more
important and selected as landmarks for the next coarser level. The number of
landmarks is selected in a data-driven manner, based on this importance. The AoI
of a landmark is defined by a second set of random walks started from all nodes
(data points or landmarks on the preceding level). Here, the length is not
predefined. Rather, once a landmark is reached, the random walk terminates. The
influence on the node is then defined for every reached landmark as the fraction of
walks that terminated in that landmark. Inversely, the AoI for each landmark is
defined as the set of all nodes that reached this landmark at least once in this second
set of random walks. Consequently, since multiple random walks initiated at the
same node can end in different nodes, the AoIs of different landmarks can overlap.
We use this overlap to define a new neighborhood graph at the levels above the
data level. Here, two nodes in the graph corresponding to landmarks at this level
are connected if they have overlapping AoIs, where the link between the nodes is
weighted by the number of data points in the overlapping area. This process is
carried out iteratively, until a predefined number of hierarchical levels has been
constructed. For the full technical details, we refer to our previous work13.
HSNE implementation in Cytosplore+HSNE. We implemented our integrated
analysis tool Cytosplore+HSNE using a combination of C + + , javascript and
OpenGL. All computationally demanding parts are implemented in C + + and
make use of parallelization, where possible. The density estimation and GMS
clustering make use of the graphics processing unit (GPU), as described in our
original publication on Cytosplore29, if possible, allowing clustering of millions of
points in less than a second. We implemented the visualizations of the embedding
in OpenGL on the GPU, for optimal performance, and less computational
demanding visualizations, such as the heatmap, in javascript. We implemented the
HSNE algorithm in C + + , as presented in ref. 13. Since we use the sparse data
structures, memory consumption strongly depends on the data complexity. Max-
imum memory consumption during the construction of a four level hierarchy plus
overview embedding of the 841,644 cell VorteX data set was 1,684 MB, construc-
tion of a five-level hierarchy of our human inflammatory intestinal diseases data
set, consisting of 5,220,347 cells required a maximum of 9,357 MB of main
memory, and finally, the 15,299,616 cell Phenograph data set required a maximum
of 24.3 GB of memory during the computation of a five-level hierarchy plus the
overview embedding. Computation times for the described hierarchies plus the first
level embedding after 1,000 iterations were 4 min, 29 min, and, 3 h and 37 min,
respectively, on a HP Z440 workstation with a single intel Xeon E5-1620 v3 CPU (4
cores) clocked at 3.5 Ghz, 64 GB of main memory and an nVidia Geforce GTX 980
GPU with 4 GB of memory, running Windows 7.
Human gastrointestinal disorders mass cytometry data set. Detailed descrip-
tion of the mass cytometry data set on human gastrointestinal disorders can be
found in our previous work14. In brief, samples (N=102) were collected from
patients who were undergoing routine diagnostic endoscopies. The cells from the
epithelium and lamina propria were isolated from two or three intestinal biopsies
by treatment with EDTA followed by a collagenase mix under rotation at 37 °C. We
analyzed single-cell suspensions from biological samples including duodenum
biopsies (N=36), rectum biopsies (N=13), perianal fistulas (N=6), and PBMC
from control individuals (N=15) and from patients with inflammatory intestinal
diseases (celiac disease (CeD), N=13; RCD type II (RCDII), N=5; enteropathy-
associated T-cell lymphoma type II (EATLII), N=1 and Crohn’s disease (Crohn),
N=10). A CyTOF panel of 32 metal isotope-tagged monoclonal antibodies was
designed to obtain a global overview of the heterogeneity of the innate and adaptive
immune system. Primary antibody metal-conjugates were either purchased or
conjugated in-house. Procedures for mass cytometry antibody staining and data
acquisition were carried out as previously described27. CyTOF data were acquired
and analyzed on-the-fly, using dual-count mode and noise-reduction on. All other
settings were either default settings or optimized with a tuning solution. After data
acquisition, the mass bead signal was used to normalize the short-term signal
fluctuations with the reference EQ passport P13H2302 during the course of each
experiment and the bead events were removed30.
Processing of mass cytometry data. We transformed data from the human
inflammatory intestinal diseases data set using hyperbolic arcsin with a cofactor of
5 directly within Cytosplore+HSNE. We discriminated live, single CD45+immune
cells with DNA stains and event length for the human inflammatory intestinal
diseases study. We analyzed other data (Phenograph and VorteX data sets) as was
available, except the transformation using hyperbolic arcsin with a cofactor of 5.
Cytosplore+HSNE analysis. Cytosplore+HSNE facilitates the complete exploration
pipeline in an integrated manner (see Supplementary Movie 1). All presented tools
are available for every step of the exploration and every level of the hierarchy. Data
analysis in Cytosplore+HSNE included the following steps: We applied the arcsin
transform with a cofactor of five upon loading the data sets. After that, we started a
new HSNE analysis and defined the markers that should be used for the similarity
computation. We used markers CD3, CD4, CD7, CD8a, CD8b, CD11b, CD11c,
CD14, CD19, CD25, CD27, CD28, CD34, CD38, CD45, CD45RA, CD56, CD103,
CD122, CD123, CD127 CD161, CCR6, CCR7, c-KIT, CRTH2, IL-15Ra, IL-21R,
NKp46, PD-1, TCRab, and TCRgd for the human inflammatory intestinal diseases
data set, all available markers for the bone marrow benchmark dataset, surface
markers CD3, CD7, CD11b, CD15, CD19, CD33, CD34, CD38, CD41, CD44,
CD45, CD47, CD64, CD117, CD123 and HLA-DR for the Phenograph dataset, and
markers CD3, CD4, CD5, CD8, CD11b, CD11c, CD16/32, CD19, CD23, CD25,
CD27, CD34, CD43, CD44, CD45.2, CD49b, CD64, CD103, CD115, CD138,
CD150, 120g8, B220, CCR7, c-KIT, F4/80, FceR1a, Foxp3, IgD, IgM, Ly6C, Ly6G,
MHCII, NKp46, Sca1, SiglecF, TCRb, TCRgd and Ter119 to construct the hier-
archy for the VorteX data set. We used the standard parameters for the hierarchy
construction; number of random walks for landmark selection: N=100, random
walk length: L=15, number of random walks for influence computation: N=15.
For any clustering that occurred the GMS grid size was set to S=256 ref. 2. The
reduction factor from one level in the hierarchy to the next coarser level is com-
pletely data-driven. In our experiments with mass cytometry data, the number of
landmarks was consistently reduced by roughly one order of magnitude from one
level to the next. Embeddings consisting of only a few hundred points usually
provide little insight. Therefore, we defined the number of levels such that the
overview level could be expected to consist of in the order of 1,000 landmarks
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meaning N=5 for the human inflammatory intestinal diseases data set and Phe-
nograph data set, N=3 for the bone marrow benchmark data set, and N=4 for the
VorteX data set. Building the hierarchy automatically creates a visualization of the
overview level using BH-SNE. Cytosplore+HSNE enables color coding of the land-
marks using expression (e.g., Fig. 3a) of any provided markers or by sample. For
example, we created the clinical feature (e.g., Fig. 3c, bottom-left panel) and blood/
intestine (e.g., Fig. 3c, bottom-right panel) color schemes based on samples for the
human inflammatory intestinal diseases data set within Cytosplore+HSNE, and for
the Phenograph data set, we created a color scheme that represented the sample
coloring as provided in ref. 4(Supplementary Fig. 7). For zooming into the data, we
generally selected cells based on visible clusters, either using manual selection or by
selecting clusters derived by using the GMS clustering. For the VorteX data set, we
clustered the third level embedding (Supplementary Fig. 8). We specified a kernel
size of 0.18 of the embedding size, to match the 48 clusters created by the X-shift
clustering described in ref. 5, resulting in 50 clusters.
For subset classification, we first cluster the embedding at a given level using the
GMS clustering. Next, we inspect the clustering by using the integrated descriptive
marker statistics and heatmap visualization. If there is still meaningful variation of
the marker expression within clusters, we zoom further into these clusters. If
clusters are phenotypically homogeneous, the corresponding cell types are defined
by inspecting the full marker expression profile in the heatmap and then the cluster
is exported from any level in the hierarchy.
Data availability. The gastrointestinal mass cytometry data set that supports the
findings of this study is publicly available on Cytobank, experiment no 60564.
https://community.cytobank.org/cytobank/experiments/60564. The source code of
the HSNE library, written in C+ +, is available at https://github.com/Nicola17/
High-Dimensional-Inspector. Furthermore, we provide a Cytosplore+HSNE installer
for Windows, allowing exploration of several million cells, for academic use at
https://www.cytosplore.org.
Received: 16 June 2017 Accepted: 25 September 2017
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Acknowledgements
The research leading to these results has received funding from Leiden University
Medical Center, the Netherlands Organization for Scientific Research (ZonMW grant
91112008) and the Technology Foundation STW, the Netherlands (VAnPIRe; grant
12720, and Genes in Space; grant 12721). We thank Drs M.W. Schilham, M. Yazdan-
bakhsh, J. Goeman, K. Schepers, J. van Bergen and S.E. de Jong for critical review of the
manuscript and B. van Lew for narrating the Supplementary Movie 1.
Author contributions
V.v.U., T.H., N.P., F.K., A.V. and B.P.F.L.: Conceived the study. T.H., N.P., A.V. and
B.P.F.L.: Developed the HSNE method and implementation in Cytosplore+HSNE. V.v.U.
and F.K.: Performed the biological analysis and interpretation. T.H.: Performed the
t-SNE scalability analysis and comparison. V.v.U.: Performed the hierarchy robustness
analysis. V.v.U. and T.H.: Performed the comparison with other methods. N.L., M.J.T.R.
and E.E.: Provided conceptual input. V.v.U., T.H., N.P., F.K., A.V. and B.L.: Wrote the
manuscript. All authors discussed the results and commented on the manuscript.
Additional information
Supplementary Information accompanies this paper at doi:10.1038/s41467-017-01689-9.
Competing interests: The authors declare no competing financial interests.
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ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-01689-9
10 NATURE COMMUNICATIONS |8: 1740 |DOI: 10.1038/s41467-017-01689-9 |www.nature.com/naturecommunications
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