 Methodology article
 Open Access
 Published:
Detection and visualization of communities in mass spectrometry imaging data
BMC Bioinformatics volume 20, Article number: 303 (2019)
Abstract
Background
The spatial distribution and colocalization of functionally related metabolites is analysed in order to investigate the spatial (and functional) aspects of molecular networks. We propose to consider community detection for the analysis of m/zimages to group molecules with correlative spatial distribution into communities so they hint at functional networks or pathway activity. To detect communities, we investigate a spectral approach by optimizing the modularity measure. We present an analysis pipeline and an online interactive visualization tool to facilitate explorative analysis of the results. The approach is illustrated with synthetical benchmark data and two real world data sets (barley seed and glioblastoma section).
Results
For the barley sample data set, our approach is able to reproduce the findings of a previous work that identified groups of molecules with distributions that correlate with anatomical structures of the barley seed. The analysis of glioblastoma section data revealed that some molecular compositions are locally focused, indicating the existence of a meaningful separation in at least two areas. This result is in line with the prior histological knowledge. In addition to confirming prior findings, the resulting graph structures revealed new subcommunities of m/zimages (i.e. metabolites) with more detailed distribution patterns. Another result of our work is the development of an interactive webtool called GRINE (Analysis of GRaph mapped Image Data NEtworks).
Conclusions
The proposed method was successfully applied to identify molecular communities of laterally colocalized molecules. For both application examples, the detected communities showed inherent substructures that could easily be investigated with the proposed visualization tool. This shows the potential of this approach as a complementary addition to pixel clustering methods.
Introduction
Matrixassisted laser desorption ionization mass spectrometry imaging (MALDIMSI) is a rapidly developing technology for investigating the lateral distribution of molecules in biological samples in form of multivariate bioimages [1].
Due to the technological improvements and the increased utilization of MALDIMSI, the daily amount of generated data is constantly increasing [2]. Since the complete interpretation cannot be automated, semiautomated and assistive computational methods appear promising and are in the focus of our research.
Different methods for grouping MSI data have already been investigated for the analysis of MSI data, such as: kmeans [3], hierarchical clustering [4], hierarchical hyperbolic selforganizing maps [5], high dimensional discriminant clustering [6], or probabilistic latent semantic analysis [7]. Many of these studies focus on clustering of all spectra in one data set to achieve a segmentation map, i.e. the partition of the image into regions with high intrinsic spectra similarity [5, 6]. In other words: most approaches focus on spectral similarity to group pixels.
The approach presented in this paper focuses on the grouping of molecules into molecular communities. We assume that many functionally related molecules may feature a similar lateral distribution in the sample. Thus, our method groups molecules into communities based on the similarity of their m/zimages. Graphs are well known data structures in biology. Therefore, we propose to use community detection for grouping [8, 9], also known as graph clustering. In our approach, one graph represents one MSI data set of N_{V}m/zimages. The N_{V}m/zimages are usually selected by a user and/or an automated selection of N_{V} peaks. A node v_{i} of the graph corresponds to one m/zimage \(I_{(m/z)_{i}}\), with i∈1,…,N_{V}, where:
N_{V}=#nodes and #nodes=#m/zimages.
Each edge e_{k}={v_{i},v_{j}}, with:
has a weight w_{ij}, which represents the similarity of the spatial signal distribution:
between the m/zimages of nodes v_{i} and v_{j}. In its initial form the graph is fully connected. Our goal is to identify communities of similar spatial distribution in order to identify groups of functionally related molecules. The method is illustrated in Fig. 1 for a hypothetical data set of N_{V}=7 images and an adjacency matrix leading to three communities.
To the best of our knowledge, community detection is a new approach for MALDIMSI data. It provides an uncommon view on the data as we focus on groups of similar spatial distributions rather than spectra similarity (pixel similarity). Few previous works have already shown the benefit of the analysis of spatial distributions in MSI ([10, 11]). Moreover, our approach provides a graph structure that serves as an additional source of information.
To tackle the problem of finding communities of m/zimages featuring a similar spatial signal distribution, we developed a modular analysis pipeline consisting of five major blocks : 1. data preprocessing, 2. computation of a N_{V}×N_{V} similarity matrix S, 3. transforming the similarity matrix into an N_{V}×N_{V} adjacency matrix A, 4. community detection and 5. interactive visualization. Step 5 aims to obtain additional information from the graph that is not available through the community detection result itself.
Methods
Data sets
MALDIMSI data forms a three dimensional data cube, where the x–axis and the y–axis represent the lateral coordinates (pixels), which can be represented as intensity images also called m/zimages, while the z–axis represents the mass spectra information. In this study three data sets are used. The first one is a synthetical benchmark data set and consists of nine generated 2D gaussians (D_{G}) (please find details below), the second data set (D_{B}) was gathered from a germinating barley seed timeline experiment [12] and the third one (D_{T}) was recorded from a section of a human glioblastoma tumor [13]. D_{B} and D_{T} are inhouse produced data sets.
D_{G} consists of nine synthetic m/zimages \(I^{\text {(gs)}}_{0} \dots I^{\text {(gs)}}_{8}\) and is a synthetical 9×205×190 MSI toy data cube. Each image contains a single localized 2D gaussian intensity distribution. The gaussians were initialized with the same size, a slightly different amplitude and were placed in groups of three:
at three different spatial locations \(L^{\text {(gs)}}_{0}\), \(L^{\text {(gs)}}_{1}\), \(L^{\text {(gs)}}_{2}\), respectively. The placement is made in such a way that it is ensured that the three groups overlap with each other in all possible combinations. This is followed by a small random distortion of the position, x size and y size, combined with a randomized rotation. A sketch of the gaussians and their variation is shown in Fig. 2.
If we think of a biological analogy for this experiment, each distorted gaussian represents the distribution of a different molecule. Each location \(L^{\text {gs}}_{i}\), with i=0,1,2, represents the area of a spatially bound metabolic process referred to as pseudonetwork, meaning that the molecules distributed in this area are likely to take part in this process.
The original data output of D_{B} and D_{T} were transformed to the form: D=N_{P}×N_{V}, where N_{V} is the dimension of vector \(\mathbf {x} \in \mathbb {R}^{N_{\mathrm {V}}}\), representing the spectrum information and N_{P} is the dimension of vector \(\mathbf {p} \in \mathbb {N}^{(m \times n)}\) with m and n are width and height of the visual field, representing the lateral information. To be more precise, the elements of p include only the measuring coordinates of the MALDI procedure, i.e. pixel grid cells. Regarding the rendered m/zimage, (x_{i},y_{j}) are pixels matching the area of the measured sample. Furthermore, in our data sets the mass spectra information \(\mathbf {x} = (x_{0}, \dots, x_{N_{\mathrm {V}}1})\), called m/zfeature vector, does not represent the whole originally measured spectra, since a set of N_{V} interesting m/zvalues were preselected by three of the authors (MG, HB, KN) based on their tissue specific and nonhomogenous distribution within the tissue section. Applied to D_{B} and D_{T} this results in a dimensionality of:
The preprocessing finishes with winsorizing the upper 1% of intensities for each image:
where Q_{99} is the 99th quantile.
Analysis pipeline
To compute the similarity matrix S we propose to apply the Pearson correlation coefficient:
where cov(p_{i},p_{j}) is the covariance of the intensity images p_{i}, p_{j} of the nodes (i.e. metabolites) v_{i}, v_{j} and \(\sigma _{\mathbf {p}_{i}}\phantom {\dot {i}\!}\), \(\sigma _{\mathbf {p}_{j}}\phantom {\dot {i}\!}\) are the standard deviations of p_{i}, p_{j}, respectively. The Pearson correlation coefficient is a commonly used similarity measure in the area of MALDI imaging analysis [14–17] and provides a straight forward interpretation. The result is a similarity matrix S, with S_{i,j}=w_{ij}. Please note that also other symmetric similarity measures can be applied here, such as mutual information or cosine similarity. For more information about considered alternatives we would like to refer the interested reader to S17 of the Additional file 1.
Next, we transform the similarity matrix into an adjacency matrix (step 3) S→A, where A is a much sparser adjacency matrix by thresholding with t_{S}:
The objective is to filter out edges with values too low, so that we can assume that these are unlikely to represent a biologically relevant similarity. However, the selection of t_{S} is a nontrivial task. To avoid time consuming manual tuning we propose a strategy which is inspired by other works on biological network analysis [18–20]. The basic idea is to define an objective function that leads to an adequate threshold after optimization. The objective function is based on quantitative graph properties (QGP). Three QGPs are selected and combined (see [21] for an overview) to determine t_{S}. The total number of edges N_{E}, the average clustering coefficient (ζ) [22] and the global efficiency (ξ) [23].
To calculate t_{S} we define a vector of candidate thresholds:
where t_{min} and t_{max} are the minimum and maximum threshold, respectively and t_{Δ}=t_{i}−t_{i−1} is the step size to reach from t_{min} to t_{max}. [t_{min},t_{max}] defines the interval of threshold candidates in which we search for the best possible threshold to reduce the edges in our network. The interval is explored in a discrete manner. This implies that the resolution of the threshold detection is defined by t_{Δ}, i.e. the distance between two consecutive points t_{i} to t_{i+1} in [t_{min},t_{max}].
We calculate N_{E}, ζ and ξ on each graph of an adjacency matrix A(t_{i}) and arrange the results in vectors \(\phantom {\dot {i}\!}\mathbf {\nu }^{N_{\mathrm {E}}}\), ν^{ζ} and ν^{ξ}, respectively. Next, we use \(\phantom {\dot {i}\!}\mathbf {\nu }^{N_{\mathrm {E}}} \mapsto [0,1]\) as baseline to adjust ν^{ζ} and ν^{ξ}:
We create a mean centered matrix X=[η^{ζ},η^{ξ}] and apply PCA as a weighting method. Therefore we calculate y, which is the projection of X on the first PCA component:
where X^{c} is the mean centered version of X, {u_{i}} are the eigenvectors of the covariance matrix X^{cov} of X^{c} and λ_{i} are their respective eigenvalues labeled in decreasing order, λ_{0}≥λ_{1}≥…. To determine the final threshold we search for the candidate threshold for which the value of y is maximized. This leads to maximizing the weighted combination of the baselined average clustering coefficient ζ and the global efficiency ξ. Hence, we can set t_{S}, with:
Since the primary objective is to achieve dense communities, it is a good choice to optimize a segregation measure like ζ. Nevertheless, we do not want to neglect the information provided from edges between communities and integrate ξ, which scales with integration. We use PCA as a weighting method because by construction ζ shows a higher variance than ξ. This leads to a stronger weighting. The idea to combine segregation and integration is based on the smallworld property, which occurs frequently in biological networks [19]. The smallworld property describes a graph structure of densely connected subgraphs that are interconnected by a robust amount of edges.
N_{E} serves as a baseline to avoid the effect that low thresholds produce high values for ζ and ξ, which is induced by the construction of these measures. This way the applied measures scale rather with structural properties than with the amount of edges. Since Pearson correlation (Eq. 2) serves as our similarity measure, we set:
t_{min}=−1, t_{max}=1, Δ=0.1.
For t_{min}, t_{max}, and t_{Δ} one has to balance computation time and resolution.
For considered alternatives we refer the interested reader to the Additional file 1: S17.
Now, A represents an undirected, unweighted graph G, which serves as basis for the community detection. In G each node v_{i}, with i=1,…,N_{V}, where N_{V}=#nodes, corresponds to a single m/zimage and is called m/znode, while each edge e_{k}={v_{i},v_{j}} indicates that: w_{ij}>t_{S}, with:
k=1,…,N_{E}; i,j∈{1,…,N_{V}} and N_{E}=#edges.
For community detection we use the leading eigenvector method [8, 9]. This method proceeds in a divisive style and maximizes a measure called modularity [24]. Since this is a divisive method, for initialization each m/znode v_{i} is assigned into the same community c, with:
c∈1,…,N_{C} and \(v_{i}=v_{i}^{c=1} \ \forall \ i\),
where N_{C}=#communities.
Thereafter, the method proceeds with:

1
For each existing community c its modularity matrix M^{(c)} is calculated. Informally speaking, for each pair of vertices (v_{i},v_{j}) the respective modularity matrix entry \(M_{i,j}^{(c)}\) shows the existing number of edges substracted by the expected number of edges between these vertices (for more detail see [8, 9]).

2
The leading eigenvector u^{(c)} of M^{(c)} is calculated, which is the eigenvector corresponding to the largest eigenvalue \(\lambda _{\text {max}}^{(c)}\).

3

(a)
If λ^{(c)}>0: All \(v_{i}^{(c)}\) are partitioned into two new communities by:
$$v_{i}^{c} = \begin{cases} v_{i}^{(c)}, & \text{if}\ \mathbf{u}_{i} \geq 0 \\ v_{i}^{(c')}, & \text{otherwise} \end{cases} $$ 
(b)
else: label \(v_{i}^{(c)}\) as “indivisible” and continue with a divisible community.

(a)
The procedure repeats for each community until all are labeled as “indivisible”. λ=0 is used as stop criteria as its u=(1,…,1), which means that the best division is to set all v_{i} in c and none in c^{′}, i.e. the best division is no division.
It is important to mention that the original work [8, 9] does not explicitly mention how to handle disconnected components. However, for MSI data sets disconnected components can be assumed to be quite common. In order to deal with this problem we propose a slight modification of the algorithm, by changing the initialization. Instead of initializing every m/znode in one community, we search for connected components and set each connected component in its own community. Using this as initialization we follow the leading eigenvector method as described above.
For alternative community detection methods we would like to refer the interested reader again to S17 of the Additional file 1. To facilitate the description of a community size we will use the terminology of (n)Community, where n provides information about the size.
Visualization
Molecular communities are characterized by two aspects that need to be explored simultaneously: localization and network structure. To analyse the computed communities in this regard, we propose an interactive visualization framework that links two visualizations for these two aspects. The tool is referred to as GRINE (Analysis of GRaph mapped Image Data NEtworks) and can be tested for the data described in this paper using the provided links (availability or supplementary). The interface of the tool is shown in Fig. 3. The functionalities are motivated and described below.
To visualize and explore the network structure display the user can choose between two different modes: In graph mode the communities’ graph structures are visualized, starting with a community graph G^{′} (see Fig. 3a). Each community forms one node \(v_{C_{i}} = \{v_{j}\}_{i}\), where {v_{j}}_{i} is the set of all m/znodes with a community membership of i. Two community nodes are connected by a community edge \(e^{\text {(C)}}_{k}\), with:
\(\phantom {\dot {i}\!}e^{\text {(C)}}_{k} = \{v_{C_{i}},v_{C_{j}}\}\),
if there exists an edge e_{l}={v_{p},v_{q}}, with:
\(\phantom {\dot {i}\!}v_{p} \in v_{C_{i}}\) and \(v_{q} \in v_{C_{j}}\phantom {\dot {i}\!}\).
The graph is fully dragable and repositions itself by a force layout. The user has the option to expand a community to show its subgraph and edges \(e^{\text {(H)}}_{k} = \{v_{C_{i}},v_{j}\}\phantom {\dot {i}\!}\) which we refer to as hybrid. Hybrid edges are edges between m/znodes and community nodes, meaning that an m/znode of an expanded community is connected with an m/znode of a non expanded community. Each node can be selected to activate the image display.
In hierarchy mode a circle packing is applied to visualize the networks while hiding the details of the graph structures (i.e. edges). This enables users to focus on community memberships instead (see the Additional file 1: S2 for a screenshot).
To analyse the localization of communities and community members, the user selects them either in the graph or in the hierarchy mode, which triggers the visualization of their spatial distribution in the image display (see Fig. 3c and d). The upper frame (Fig. 3c) shows the community map with a pseudo coloring chosen from a menu (Fig. 3e). The community map is a summary of all images from one selected community \(I_{C_{i}} = D_{\mathbf {p},\{s_{j}\}_{i}}\), i.e. all m/zimages corresponding to m/zvalues s_{j} that are members of community C_{i}.
Community maps can be computed and visualized in two modes: In maximum projection mode the maximal intensity in the community is displayed for each pixel:
where Φ(pk′) is the intensity of pixel \(p^{\prime }_{k}\). This mode displays the total area covered by the entire community.
In averaging mode the intensity for each pixel is averaged across all images in the community:
This emphasizes the quantity of signal coverage.
The lower frame (Fig. 3d) shows the single mass map visualizing one \(I_{(m/z)_{i}}\) image (after selecting this community member in the network display or in the mass list on the far left (Fig. 3f)). The pixel intensities are rescaled for a maximum contrast to enable the visual analysis of weak mass signals.
Furthermore, there is the option to visualize the relation of community localizations with another kind of pseudocolor map, the PCA (principle component analysis) map. This visualization takes the full data set D into account and thus accounts for variances in the entire N_{V} dimensions. The R, G, B color values in the PCA map are computed with a projection of the full data set onto the three most informative principle components (details given in Additional file 1: S5). This map has been implemented to enable users to integrate global data features. In addition, PCA is a well established and familiar way to analyze high dimensional data so that it can be used as a reference despite its limitations.
Some implementation details can be found in S14 of the Additional file 1.
Finally, we would like to refer the reader to S16 of the Additional file 1 for further information on how the similarity measure, threshold selection and community detection algorithm influence each other and their impact on the downstream analysis.
Results
Weblinks to all results obtained for data sets: D_{G}, D_{B} and D_{T} can be found under Availability of data and material.
Gaussians
For the data set D_{G} an edge reduction threshold within t_{S}∈[0.6382,0.9397] was computed (see Table 1 and Eq. 4). The specific value picked inside of this interval is irrelevant, since the arg max function is maximal over the entire interval. Our community approach detects three communities that corresponds to the groups \(K^{\text {gs}}_{i}\), with i=0,1,2, meaning that we can distinguish the gaussians based on their spatial location (see Fig. 4a).
If we discuss this result in relation to our biological analogy, each group \(K^{\text {gs}}_{i}\) with distribution at \(L^{\text {gs}}_{i}\) consists of molecules that are likely to be representatives of a metabolic process located in this area. Let us remember our initial assumption that functionally related molecules feature a similar lateral distribution within the sample, i.e. metabolic processes are spatially bound. If this assumption holds, the results obtained from D_{G} indicate that our communities can help to: 1. distinguish metabolic processes based on their spatial location and 2. identify their important molecules.
Figure 4b shows kmeans segmentation maps with different k, i.e. clustering of pixel. Even with the correct number of clusters (k=4, i.e. background and three pseudonetworks) the segmentation map cannot distinguish the covered areas at the three different locations.
Compared to kmeans clustering or hierarchical clustering, our method does not require to determine the number of groups, which can be considered an advantage.
Barley
For data set D_{B} we computed the threshold t_{S}=0.7085 (Eq. 4). This results in N_{E}=789 edges, meaning a reduction of 84.376% (Table 1). Based on the resulting graph, the leading eigenvector method found N_{C}=11 communities (see Additional file 1: S4). Nine of them are interconnected, while two are singletons, i.e. nodes without any edge. Eight of the interconnected communities are (n)Communities, with n>1, the others are (1)Communities.
Most signal distributions of the community maps (Fig. 5) show a strong correlation to anatomical structures of the barley seed, which is summarized in Fig. 5e.
A view on the graph structure of C2 (Fig. 6a) reveals that this community can be divided into more detailed subcommunities (referred to as C2a  C2c). C2b shows an increased signal only at the embryo center, while the signal of C2a is less specifically distributed in the entire embryo. C2c is located between both and shows a specific signal distribution at the center and the shoot. A similar observation can be found for C5. The subgraph of C5 (Fig. 6b) shows a structure that can be distinguished into core and offshoots. A core is defined by nodes that are densely interconnected, while offshoots are reaching out from the core and are less interconnected. The core of C5 (C5c) defines the main signal distribution of this community, which extends from the scutellum into the embryo center. The three offshoots C5a, C5b, and C5d deviate from this distribution. A similar coreoffshoot differentiation can be observed in C4 (not shown).
The identification of m/zvalues based on prior examination of barley seed MSI [12] reveals a tendency for communities to mostly contain one class of molecules. C0, C1, C3 and C7 contain only hordatines and hordatine precursors, with one exception in C0, which is a lipid and three exceptions in C3, which are two unknown molecules and one lipid. C2 and C4 contain mostly carbohydrates, with four exceptions (three unknown molecules and one lipid). Further, carbohydrates in C2 are only potassium adducts and in C4 only sodium adducts. C5 and C6 contain mostly lipids, with two exceptions in C5 that are unknown molecules. The (1)Communities are unknown (C8, C9) and a lipid (C10). This indicates that similar molecules have similar spatial distributions. One reason for this could be that similar molecules are part of the same spatially bound metabolic processes.
The identification also supports the structural features of C2 and C5. C2a is composed of three unknown molecules, one lipid and one carbohydrate, while C2b consists only of carbohydrates. For C5, the two images that fit least to the main signal distribution of the community are both unknown molecules.
Glioblastoma
For data set D_{T} we computed the threshold t_{S}=0.5477 (Eq. 4). The result is N_{E}=2371 edges, i.e. a reduction of 57.394% (Table 1). Compared to the barley data set the number of edges is clearly higher, although the number of vertices is nearly equal. The reason is a higher general similarity and a lower spread of similarity values, i.e. the algorithm classifies more similarities to be relevant. This indicates a higher degree of complexity for the tissue and its respective network of functionally related molecules. The community detection result shows N_{C}=11 communities with seven of them interconnected (see Additional file 1: S4). Five are (1)Communities, the other six are (n)Communities, with n>1.
The signal distributions (Fig. 7) reveal three main patterns, which are summarized in Fig. 7e.
Similar to the results obtained for barley data, a detailed view on the graph structure reveals more detailed information (Fig. 6c). The subcommunity C6a shows a strong and specific distribution in one half of the sample. C6b is distributed notably less specific, with a slightly biased signal distribution to the same half of the sample as C6a. Both subcommunities are connected by a m/zimage (C6c) that shows a weak similarity to C6a. We assumed that C6c produces a chaining effect during the community detection.
Based on communities C6a and C8 we can conclude that the sample is functionally divided into two halves, which is in line with the PCA result (Fig. 7d) and (more important) the H&E staining information (Fig. 7a), which indicates that the tumor in this sample is side specific. We can presume that at least some molecules of C6a and C8 could be tumor specific.
Results of the publicly available mouse urinary bladder data set from msimaging.org are shown in Additional file 1: S12. There we provide some basic results without detailed biological interpretation. The results are available for exploration in our webtool. The respective link can be found in Additional file 1: S1.
Discussion
Barley
The analysis of the barley seed data set shows that the community analysis approach delivers reasonable results, i.e. the spatial localizations of the communities reflect biological compartments with distinct functions. This is in accordance with previous findings for this data set [12]. For most communities, we are able to clearly detect correlations with different anatomical structures.
In contrast to other established methods for MSI segmentation, the presented approach offers a very fine identification of the different tissues of a barley seedling based on the mass spectroscopy data. As shown in Fig. 5, the root, the center of the developing seedling, the shoot, the scutellum, and the endosperm could be identified by a unique combination of communities. This segmentation can be used to analyze the colocalization of specific single mass channels, representing known intermediates of the metabolism.
The fact that certain tissue regions or organs are represented by a number of different communities indicates that these parts of the sample are physiologically more heterogeneous than would be expected if a single m/zsignal were colocalized with that particular tissue or organ. An example for this kind of heterogeneity for the shoot can be seen in the communities C1, C7, and C10. Most interestingly, it shares communities with the root, but not with the scutellum. From a biological point of view, it can be speculated that these differences reflect metabolite compositions that are characteristic for developing tissues, as roots and shoots, versus a tissue, which is metabolically active but not further developing just like the scutellum.
The appearance of substructures in individual communities within the graphs illustrates that our graph approach is able to convey information that would remain hidden if just cluster results were considered. Interestingly, the three substructures investigated in this study show already three different kinds of motifs: Simple subgroups, coreoffshoot structures, and bridging (or chaining) structures. Therefore we believe that substructures are worth further examination.
Glioblastoma
The results of the glioblastoma data set are not as easy to interpret as those of the barley sample, which was to be expected. This is due to its morphological homogeneity, combined with heterogeneity of the cell phenotype. On the other hand the community detection yields at least one clear insight: There are groups of molecules, whose signal distribution correlate with the tumor area that was defined by a pathologist [13]. This provides candidates for subsequent biological experiments.
Regarding their community compositions, the tissue compartments classified as tumor and tumorinfiltrated in data set D_{T} are much more similar to each other than the different compartments of the barley sample. Five of the eleven communities are categorized as ubiquitous (Fig. 7), reflecting the fact that the tumor tissue is still closely related to the nontumor tissue. Four communities are tumorspecific (Fig. 7), probably induced by the localization of lactate and other tumor metabolites (see [13]). The last two communities refer to the outer border of the sample (Fig. 7), probably induced by matrix peaks.
We believe that even without any prior knowledge about the sample, like H&E staining, the results offered by this type of analysis provide a good starting point for biologists to set up further experiments.
Visualization
Our visualization tool GRINE is interactive, dynamic and responsive. This makes the usage very intuitive and almost no learning phase is required. The tool shows its main strengths in three areas. First, it combines the information of the graph domain and the image domain. Second, the interaction with the graph facilitates the focus on specific communities and allows to spot structural characteristics. Examples are: Substructures that can indicate more finely resolved communities, cluster ambiguities and potential misclusterings. Third, its possibility to show and hide information, i.e. its interactivity, allows to encode much more information in a clear way than we could achieve with static visualizations [25], e.g. average and maximum images of all communities and correlation with PCA results.
At the current time, the visualization can only deal with distinct communities, whereas the analysis pipeline can also search for overlapping ones.
Comparison to other methodological approaches
A more common approach than the one presented for the analysis of the spatial distribution of imaging data is to employ dimension reduction techniques for segmentation. We compared our method to visualizations of three different dimension reduction techniques: principal component analysis (PCA), nonnegative matrix factorization (NMF) and latent dirichlet allocation (LDA) (results are shown and discussed in Additional file 1: S13). We decided for PCA as it is probably the most prominent dimension reduction technique in biology. NMF is also a commonly used technique and does not produce negative intensity values, which can occur in PCA. LDA was chosen because it is a generalization of pLSA (probabilistic latent semantic analysis) that has been previously analysed [7].
The comparison showed that the computed visualizations reveal similar coarse grained structures as our method. It is worth noting that LDA performs better as NMF and NMF performs better than PCA. For D_{B} and D_{T} the segmentation maps of LDA reveal the most details and detected structures show the highest contrast. This is followed by the ones obtained with NMF. The PCA maps provide the lowest contrast. All three methods show distributions that correlate with the main structures of the samples. However, compared to our method they fail finding finely detailed structures like the scutellum in D_{B}.
While the results obtained with PCA, NMF and LDA share similarities with the results obtained by our proposed method, we can report some new favorable features for our approach:
First, the grouping of spatial distributions assigns each image to one group. After analysing the lateral distribution of a community image it is easy and unambiguous to identify which single m/zimages, i.e. molecules, participate in this distribution. This is much harder for PCA, NMF and LDA, where each component image consist of partial combinations of the original m/zimages.
Second, we do not need to determine the number of clusters, i.e. communities, beforehand. Our method chooses this number automatically based on the given optimization criterion (modularity). If needed, a manual decision is still possible. This is different for NMF and LDA. For those methods the number of dimensions, i.e. components, have to be predefined. Finding the most fitting number of dimensions for a given sample is a non trivial task and especially important for NMF and LDA, since the number of dimensions influences the lateral distribution of the resulting components (see Additional file 1: S13).
Third, the community images are based on simple aggregation functions. Therefore, in case of outliers or ambiguities it is easy to reevaluate the community images without them. The same counts for potential optimizations based on substructures in the clustering space.
Fourth, the network structure can reveal outliers, misclusterings, substructures and potential optimizations at first glance and allows an intuitive exploration of the clustering space.
We would also like to add the H^{2}SOM ([5]), as another segmentation method to this comparison discussion. This method is also capable to reveal detailed structures in D_{B}. A core difference is that in our method a single pixel can be a member of multiple community images. This means we can provide an ambiguous pixel labeling, which is more suitable to represent dynamic biological processes.
Conclusion
In this paper we demonstrated the general applicability of community detection as an unsupervised clustering technique for the analysis of MSI data from different types of samples. We have developed a pipeline to map lateral image data to an image similarity graph. We have also developed a new edge thresholding technique to transform a fully connected graph into a sparse one. Using lateral m/zimages as samples and their pixels as features, we utilized community detection as an example to group molecules with similar lateral distributions. By analysing the network structure with our interactive visualization, we have found finer subclusters within the detected clusters. This offers a possibility for manual refinement.
We stated the initial assumption that functionally related molecules are spatially bound. If this assumption holds, the presented way of clustering lateral imaging data provides a good starting point for targeted biological experiments. For some information on the limitations of this assumption and alternative considerations, we would like to refer to S18 of the Additional file 1.
This paper is designed as proof of concept to demonstrate the general applicability of community detection to MSI data. Therefore, we did not discuss the question of performance. Since we do not want to ignore this question entirely, we refer to S15 of the Additional file 1. There a rough analysis of the complexity is presented.
Finally, a webtool has been implemented to visualize and explain the results and to demonstrate the usefulness and benefits of the approach.
Future research Further analysis of network patterns of substructures could lead to automated ways to detect finer cluster relationships, like hierarchical structures and reveal and correct misclusterings. Another promising approach, which was not discussed in this paper, is to employ more statistical network properties for the analysis. An example could be the ratio of the ingroup degree to the outgroup degree to automatically detect very specific or very general lateral patterns. These statistics could also be used to query specific m/zimages for their statistical properties. Considering the sheer amount of network properties this offers a big area for future research.
Abbreviations
 H ^{2}SOM:

Hierarchical hyperbolic selforganizing map
 H&E:

Hematoxylin and eosin
 GRINE:

Analysis of GRaph mapped Image data NEtworks
 LDA:

Latent dirichlet allocation
 m/z :

Mass to Charge
 MALDI:

Matrixassisted laser desorption ionization
 MSI:

Mass spectrometry imaging
 NMF:

Nonnegative matrix factorization
 PCA:

Principal component analysis
 pLSA:

Probabilistic latent semantic analysis
 QGP:

Quantitative graph properties
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Acknowledgements
Not applicable.
Funding
KW is funded and JK was partially funded by the International DFG Research Training Group GRK 1906. The funding body played no role regarding the design of the study, data collection, analysis or interpretation or the writing of the manuscript.
Availability of data and materials
All webapplication results are accessible at: Gaussians, Barley, Glioblastoma via Chrome or Firefox. All links are also given explicitly in Additional file 1: S1.
Data sets and demo code for network building and community detection: Datasets,Code.
Demo code for the visualization: GRINE
A small overview video for our webtool is available at: https://www.youtube.com/watch?v=I40fe8TUijs.
Supplementary information: Supplementary data is available online.
Availability and Requirements
The source code and demos for the community detection workflow the visualization (GRINE) is available on github.
Project name: MSI Community Detection
Project home page: https://github.com/Kawue/imaging_communitydetection_demo
Operating system(s): Platform independent
Programming language: Python
Other requirements: Python 3.5 or higher
License: GNU GPLv3
Project name: GRINE
Project home page: https://github.com/Kawue/grine_demo
Operating system(s): Platform independent
Programming language: Javascript
Other requirements: Firefox or Chrome
License: GNU GPLv3
Author’s contributions
Concept and design of study and software: KW, JK, TWN. Developed and implemented software: KW. Analyzed data: KW. Evaluation and interpretation of results: KW, JK, HB, KN, TWN. Contributed data: JK, HB, KN. Pathological structure analysis of the glioblastoma sample: VHH All authors read and approved the final manuscript.
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Ethics approval and consent to participate
The collection and use of human tissue was approved by the ethical review committee of the University DuisburgEssen. All patients (donors of the human tissue) provided their informed consent in written form. We obtained the data completely anonymised.
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Not applicable.
Competing interests
The authors declare that they have no competing interests.
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Additional file
Additional file 1
Supplementary. (PDF 2459 kb)
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Wüllems, K., Kölling, J., Bednarz, H. et al. Detection and visualization of communities in mass spectrometry imaging data. BMC Bioinformatics 20, 303 (2019). https://doi.org/10.1186/s1285901928906
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DOI: https://doi.org/10.1186/s1285901928906
Keywords
 MALDI imaging
 Networks
 Clustering
 Community detection
 Visualization
 Graphs