Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm.
Here we look at some of the existing approaches to integrative clustering and how they use the assumption of a common clustering structure. The iCluster algorithm [4,5] uses a Gaussian latent variable model to infer clusters. It assumes that there is a common set of latent cluster membership variables across all datasets. Differences in structure between different datasets are accounted for only via individual noise terms, which correspond to within-dataset variances. iCluster uses the k-means algorithm to extract the actual cluster assignments given latent variable values. Using this approach, it is possible to obtain clusters where the data are well separated across all datasets. However, if two clusters are joined together in one dataset and separate in another dataset, this leads to ambiguous latent variable values and iCluster fails to infer the correct partitioning. Also, using k-means for cluster inference from latent variable values makes the algorithm strongly dependent on the correct specification of the number of clusters. We demonstrate this behaviour on a simulated dataset later in the paper. A similar algorithm to iCluster is the Joint and Individual Clustering (JIC) [6]. This algorithm uses the connection between principal component analysis and k-means clustering to integrate data from multiple datasets and infer both joint clusters and dataset-specific clusters. The dataset-specific clusters are estimated so that they are independent of the joint clusters, and thus capture independent structure that exists only within individual datasets. This algorithm still uses the assumption of one global cluster structure across the datasets, which is augmented by additional cluster structures within individual datasets that are independent of the overall global structure. Bayesian consensus clustering (BCC) [7] relaxes the assumption of a common cluster structure by allowing different datasets to follow individual local clustering models. Apart from the local clusters, BCC introduces a global cluster structure as well. This depends on the local clusters through a parameter α, which regulates how much the local clusters correspond to the global clusters. The split between global and local structure brings flexibility in modelling inconsistent cluster structures across many datasets. The consensus global cluster assignments then define a common clustering based on partial agreement between context datasets. However, this again assumes that there is a common set of clusters which are exhibited in a similar way across all datasets. Deviances from this structure are considered to be dataset-specific noise, which might lead to omission of any additional structure that is present in the data. Multiple Dataset Integration (MDI) [8] uses a different approach. This model does not assume a common clustering but it looks only at pair-wise relations between datasets. In this formulation, every dataset can have a flexible set of clusters, and a pairs of samples are considered fused, if they belong to the same set of pairs of clusters across datasets. This limits the interpretability of a solution. Although the model allows enough flexibility for combinations of clusters, it does not explicitly encourage any sharing of clusters across more than pairs of datasets. Another approach is taken by the Similarity Network Fusion (SNF) [9]. This algorithm uses an iterative approach on dataset-specific similarity networks that intensifies strong similarities and diminishes weak similarities across samples. This again implies the existence of a common structure that is consistent across the datasets. We introduce the context-dependent clustering model to address the issues outlined in the introduction. Compared to existing integrative clustering algorithms, the proposed model does not generally assume a single partitioning of the data that is consistent across heterogeneous datasets. If a process is allowed to follow different cluster structures across several contexts, we have to modify our notion of clustering. We assume that there is a set of clusters within each context (dataset), but these clusters do not generally correspond directly to each other. However, some dependency between the clusters is expected: Using the example of the HER2 oncogene from the Introduction, there are two levels of complexity we can analyse. If we consider only tumour samples which over-express HER2, the first level is formed by the individual contexts: there are two clusters in DNA amplification dataset (amplified or neutral), and one cluster when we consider mRNA expression of HER2 (over-expressed). If we assumed a globally consistent cluster structure, we would have to either artificially split the cluster of samples which over-express HER2, or artificially join the two clusters with different copy number aberrations. When we look at the problem from the global level, we get two groups of samples that behave in a different way across contexts. The behaviours are defined by different combinations of context-specific clusters. The concepts are illustrated in Fig 1. The context-dependent clustering model as proposed here uses a Bayesian clustering framework to infer both the local structure within each dataset, as well as the global structure which arises from the combination of cluster assignments. We use probabilistic framework, with hierarchical Dirichlet mixture models to model clusters across the contexts. We fit the model using Gibbs sampling. Details of the model formulation and inference algorithms are presented in the Methods section. In the next section we focus on a comparison of the proposed algorithm with alternative context dependent clustering approaches on simulated as well as on real-world datasets.
In this section, we look at how the proposed context-dependent clustering method performs in a situation where there is no single common clustering structure. We compare its performance with popular alternative clustering algorithms on a set of simulated data with various degrees of dependence between contexts. The simulated data are formed by two 1-dimensional datasets, which represent a dataset with two contexts. Each context contains two clusters: both clusters are normally distributed with unit variance but with different means. Cluster 1 in both contexts is centred at -2 and Cluster 2 is centred at 2. The two clusters are well separated and they do not overlap significantly. We combine the clusters to simulate different degrees of independence between context-specific cluster structures. We generate 100 data sets each with 200 samples according to the following procedure. For each data set we sample a mixture probability p uniformly from the interval (0,0. 5). For the first group of 100 samples we sample each of the two context values independently from N (- 2,1) or from N (2,1) with probability 1 − p and p, respectively, that is, since p < 0. 5 mostly from Cluster 1, but occasionally also from Cluster 2. For the next group of 100 samples we sample the each of two context values independently from N (- 2,1) or from N (2,1) with probability p and 1 − p, that is, mostly from Cluster 2 this time, but occasionally from Cluster 1. For mixture probabilities p close to 0, the clusterings of the two contexts largely agree by putting the first 100 samples into Cluster 1, and the rest into Cluster 2, leading to two global clusters. At the other extreme, if p is close to 0. 5, cluster assignments are more or less random and there is little agreement between contexts, leading to four global clusters. Fig 2 shows data sampled from the two opposing scenarios. If the data distributions are fully dependent across the contexts, we get two global clusters (Fig 2a). Because the data are assigned to the same cluster in both contexts, there is a single common cluster structure. On the other hand, if the two contexts are independent, we get four global clusters (Fig 2b) which correspond to the combinations of context-specific cluster assignments. This scenario is difficult to model if we look at individual contexts separately. As we discussed above, most existing algorithms assume a common set of clusters that are shared across all contexts, with only a limited deviation from this structure. The simulated data illustrate what happens when this assumption does not hold, i. e. when there are different degrees of dependence between the contexts. We compare clusternomics with existing algorithms to see how they adapt to different scenarios where the assumption of a single common cluster structure is not satisfied. In this section we apply the context-dependent clustering to a breast cancer dataset obtained from The Cancer Genome Atlas (TCGA) database. The dataset contains measurements of 348 patients diagnosed with breast cancer, and it comprises of four different data types: DNA methylation for 574 probes, RNA gene expression for 645 genes, expression for 423 microRNA molecules and reverse-phase protein array (RPPA) measurements for 171 proteins. The dataset was originally presented in Koboldt et al. [12], and it was also previously used to evaluate integrative clustering methods in Lock and Dunson [7]. An advantage of the dataset is that it also contains clinical information on the patients including their survival. This information can be used to validate the clustering results. We used the proposed context-dependent clustering algorithm to identify subtypes of breast cancer across the four data contexts. We applied the proposed algorithm with several different settings of both the global number of clusters, and the local context-specific number of clusters. The details of the algorithm’s settings are provided in S1 Appendix. To provide an overview of the results, we first look at a specific clustering obtained from the model to illustrate the working of the algorithm. Then we look at consistency of the results with respect to varying number of clusters, and also at results from two other cancer datasets from TCGA. To illustrate the results from the model, we use an example with the number of local clusters set to 3 in all the four contexts (gene expression, DNA methylation, miRNA expression and RPPA) and 18 global clusters. The number of global clusters acts as an upper bound on the number of clusters that can be represented in the data by the clustering model. The number of clusters that the model identified in the presented result was 16. Fig 5 shows the size distribution of these 16 clusters. There are several larger clusters and a larger number of smaller clusters. The global combinatorial clusters identified by the model showed clinical significance in terms of survival probabilities. Fig 6 shows the different survival curves corresponding to each of the clusters. The survival probabilities in each cluster are significantly different with p = 0. 038 using the log-rank test with a null hypothesis that assumes that the survival rates are the same. Here, identification of the global structure helps clustering within individual datasets. Looking at the local cluster structure within each dataset, the differences in cluster survival curves are not individually significant. By looking at the overall global structure, we can identify groups that are characterised by different survival outcomes. Fig 7 shows the global clusters as they appear in the four individual contexts in the first two principal components. The clusters are better defined for the gene expression and the protein assay context. However, the samples corresponding to each cluster also occupy distinguishable regions in the remaining contexts. Because the plots show only the first two principal components, we also provide an overview of variance captured by these components in S1 Appendix. In contrast to the global clusters in Figs 7 and 8 shows the inferred local clusters within each dataset. The clusters are well defined in all contexts except the miRNA expression dataset, where the model did not identify any structure. However, by sharing information from other contexts we see that some of the global clusters are distinguishable in this context as well. For example, in Fig 7c Cluster 3 (violet) occupies a distinguishable region in the miRNA context although the dataset itself does not show any pronounced clustering tendency within the first two principal components. This indicates that the identified clusters represent some feature of the underlying process even within datasets that, by themselves, contain only a vague structure. To highlight some of the features of the presented context-dependent clustering model, we look at two clusters in more detail, namely clusters 1 and 6. Fig 9 shows the two highlighted clusters. We can see that they are similar in all contexts except gene expression, where they belong to different clusters. This is the type of structure where two clusters are merged into one in some of the contexts and separate in other contexts, which we illustrated at the beginning of this chapter using the example of the HER2 gene. This type of structure relates to the interplay of different levels in the underlying biological process. The differences between the two clusters are also biologically relevant. Fig 10 shows the highlighted survival curves for the two clusters. The green cluster (Cluster 6) contains 40 samples and has significantly different survival outcomes from the dark blue cluster (Cluster 1) containing 19 patient samples, with a log-rank test p-value of 0. 012. The presented result from the model shows that the identified structure is clinically relevant. The assumptions behind the Clusternomics algorithm also help identify combined global clusters that would be missed by other algorithms, as demonstrated on the simulated datasets. The illustration of the results looked at a specific size of the model. The algorithm also allows the user to specify the number of clusters both on the local context-specific level, and on the global level. We fitted the model with varying numbers of clusters on both levels and we look at the stability of results with respect to different parameter settings. The results show that the estimated global clustering structure is stable when the number of clusters is large enough to give the model sufficient flexibility to fit the data. Fig 11 shows a comparison of log likelihoods of the fitted models for 10 different numbers of global clusters. The maximum of the log likelihood corresponds to the model with 18 global clusters that we highlighted as an example in the previous section. The same model also corresponds to the lowest p-value with respect to the differences in the survival function, as shown in Fig 11b. The number of global clusters specified in the algorithm serves only as the upper limit on the number of occupied clusters. To investigate this relation we also look at the number of clusters that are actually occupied for different settings of the number of global clusters. Fig 12 shows the posterior average number of clusters that had samples assigned to them. The figure shows both the average total number of occupied clusters across Gibbs samples for each run, and the average number of large clusters (clusters that have at least five samples assigned to them). The number of large clusters is more reflective of the actual underlying structure, because Dirichlet mixture models with large number of components are inconsistent with respect to the true number of clusters [11]. Fig 12 shows that both the number of larger clusters and the number of all clusters is stable between the different settings, when the model is saturated (with 18 or more global clusters) and provided that the corresponding chain reached convergence. The only exception is the solution with 45 global clusters where the algorithm identified an alternative clustering solution, with lower likelihood. In this case the probabilistic algorithm remained in a local optimum. We provide additional analysis of convergence of the presented algorithm in S1 Appendix. Even though the number of estimated clusters changes with parameter settings, the cluster structures might still be very similar. In order to explore these similarities, the ARI between pairs of clusterings of different sizes is displayed graphically in Fig 13. As the area of high ARI values (that is, similarity of clusterings) in the upper right corner indicates, the solution with 18 global clusters is a part of a larger group of solutions with similar cluster assignments. The result with 18 global clusters corresponds to the maximal log likelihood also corresponds to the smallest number of global clusters for which the results stabilise and then remain consistent for larger numbers of global clusters. The result with 45 global clusters is again an outlier. Fig 14 shows the pairwise ARI values for local cluster assignments. The local assignments become stable at various sizes of the model. Generally, the larger number of possible global clusters give the model more flexibility to model cluster structures in individual datasets, as opposed to modelling a common global structure. Again, 18 global clusters represent the parameter setting where all the context-specific clusters converge to similar cluster assignments. We also look at the effect of setting different numbers of local clusters. We look in detail at 3,4 and 5 local clusters in every local context-specific dataset. Fig 15 shows the agreement between the results as measured by the ARI. All the cluster assignments are relatively consistent given different model sizes with high pairwise ARI values. In general, the number of clusters tends to saturate for larger sizes of the model. Because the model asymptotically approaches a Dirichlet process when the number of global/local clusters is large, the model automatically infers the number of clusters that is needed to represent the data (see the Methods section for details). However, smaller sizes of the model are more computationally efficient in real-world scenarios. To infer a reliable clustering of real-world data, it is necessary to explore several different settings of the model’s parameters, where stability of the clustering can serve as an indicator of clustering tendencies in the dataset [13]. To assist with selecting the number of clusters, the package clusternomics also provides the Deviance Information Criterion (DIC, [14]). DIC is a criterion for model selection for Bayesian models, combining posterior likelihood with penalty for model complexity. Number of clusters should be chosen so that the value of DIC is minimized. Fig 16 shows the DIC values for the number of global clusters for the breast cancer dataset. Based on this measure, the optimal number of clusters is 18, which is also the value where the cluster results become stable. To evaluate the general utility of the model, we also examined two additional smaller cancer datasets from The Cancer Genome Atlas repository: The datasets were normalised to zero mean and unit variance for every feature, but the total size of the data was not reduced. For both datasets we again fit models with 3 to 5 local clusters in each context and varying numbers of global clusters. In this case, the size of the problem in terms of number of genes and DNA methylation loci is problematic for the iCluster algorithm. For larger problems with more features it becomes increasingly memory intensive (at least 60 GB of memory were required to run iCluster on the lung dataset, and the algorithm did not terminate in a reasonable period of time). Figs 17 and 18 show the summary of the results on the lung cancer dataset, using the clinical survival information. The differences in survival prospects in the clusters are statistically significant for all clusterings of this dataset. The consistency of results reveals that there are three versions of stable cluster assignments across the models of different sizes. Figs 19 and 20 show similar results for the kidney cancer dataset. Here, the survival p-values drop below the significance threshold when the cluster assignments become more stable and consistent. In both cancer datasets, the Clusternomics algorithm identified clinically relevant clusters.
The context-dependent clustering model explicitly represents both the local clusters within each dataset (local context), and the global structure that emerges when looking at the combination of cluster assignments across the individual datasets. When we consider a local structure within several datasets, each dataset has its own context-specific set of clusters. When we look at the combination of the context-specific clusters, we get a combined structure which defines clusters on the global level while keeping information about cluster membership on the local level. For example, if one context (dataset) contains two clusters of samples labelled 1 and 2, and second dataset contains three clusters, labelled A, B and C, we get a combined structure with six potential global clusters where each cluster corresponds a combination of assignments on the local level: (1, A), (2, A), (1, B), (2, B), (1, C), (2, C). Fig 21 provides a schematic illustration of the concepts. Using this formulation, we can model groups of data that are joined in one context and separated in another context, because they correspond to different global clusters. We use this intuition to develop the context-dependent clustering model, which is based on a Bayesian probabilistic clustering. The algorithm is based on Dirichlet mixture models [15] and their infinite generalisation, the Dirichlet process mixture model [16,17]. The probabilistic model accounts both for probabilities of individual samples belonging to a specific cluster on the local level, and for the global probabilities of samples belonging to a global cluster. The global clusters are modelled as a combination of local clusters, and bound together by a Bayesian hierarchical model. This assures that a local cluster assignment changes the posterior probabilities of corresponding global cluster assignments. Continuing with the two dataset example, when a sample is assigned to cluster 1 in the first dataset, it increases the posterior probability of the cluster within the first dataset, but it also increases the probability of global clusters (1, A), (1, B) and (1, C). This dependence is defined by the Bayesian hierarchical model, and encodes the objectives stated in the Introduction. In this section we introduce two alternative Bayesian probabilistic models for context-specific clustering. The first model looks explicitly at all possible combinations of local clusters which define the global combinatorial clusters. The second formulation of context-dependent clustering only models a restricted set of combinations of local clusters, that are data-driven. Both models are asymptotically equivalent (for details see S1 Appendix). The first formulation of the model provides an intuition that leads to the second formulation of the model. Because the second formulation uses only a smaller number of local clusters, it is more computationally efficient than the first formulation, and it was therefore used to compute the results presented in the Results section. We introduce the following notation: xn, n = 1, …, N, are data items where xn is composed of a set of observed values coming from contexts c = 1, …, C: x n = (x n (1), ⋯, x n (C) ) For example, for C = 2, xn may represent a tumour sample from patient n with gene expression values x n (1) and DNA copy number states x n (2). Using the basic model presented in the preceding section we can now construct a composite model for integrative clustering, which we call Context-Dependent Clustering (CDC). To keep the notation simple, we first present the model for only two contexts, c ∈ {1,2}. We start by specifying two mixture distributions as defined in the previous section, one for each context. Each context has its own mixture weights π (1) and π (2), with symmetric Dirichlet priors: π (1) ∼ Dirichlet (α 1 K (1), …, α 1 K (1) ) π (2) ∼ Dirichlet (α 2 K (2), …, α 2 K (2) ). These two mixture distributions form the basis of local clustering within each context. We link the two distributions together using a third mixture distribution, also with a Dirichlet prior over the mixture weights ρ. This represents the global mixture distribution, defined over the outer product of π (1) and π (2): ρ ∼ Dirichlet (γ vec (π (1) ⊗ π (2) ) ) Here the outer product of π (1) and π (2) is π (1) ⊗ π (2) = π (1) (π (2) ) T = (π 1 (1) π 1 (2) π 1 (1) π 2 (2) … π 1 (1) π K (2) π 2 (1) π 1 (2) π 2 (1) π 2 (2) … π 2 (1) π K (2) ⋮ ⋮ ⋱ ⋮ π K (1) π 1 (2) π K (1) π 2 (2) … π K (1) π K (2) ) (2) The vec operation takes a matrix and stacks its columns on top of each other to form one column vector. We use the outer product matrix in a vectorised form as the basis for the (non-symmetric) concentration parameters of the Dirichlet distribution over global mixture weights ρ. Each element of ρ corresponds to a specific pair of local cluster probabilities π i (1) and π j (2). This effectively creates a mixture model over all possible combinations of cluster assignments on the level of individual contexts. The prior probability of a data item being simultaneously assigned into cluster k in the first context and cluster l in the second context corresponds to the element s in the ρ vector which originated from the row k and column l in the outer product matrix (2): p (z n (1) = k, z n (2) = l) = ρ s where s = (k, l) We also define a composite cluster indicator variable z for each sample that represents a pair of z (c) values, one for each context c: zn∼Categorical (ρ), zn= (zn (1), zn (2) ) xn (c) ∣zn (c) =k∼F (c) (xn (c) |θk (c) ), c∈{ 1,2 }. By fitting this model to a dataset, we obtain both local clusters on the level of the original datasets (contexts) and the global composite clusters that represent different combinations of individual local cluster assignments. Local clusters for context c can be obtained by taking a projection of the z indicator variable onto the context c. Going back to the HER2 oncogene example in the Introduction, the model explicitly represents the local clusters with respect to DNA copy number changes and mRNA expression, while forming two global clusters with respect to the overall behaviour. The model satisfies the objectives presented in the Introduction. In the posterior, assignments into a context-specific cluster affect the posterior distribution of π (c), which in turn affects the probabilities of all global combinatorial clusters through the hierarchical model. For example if we look at the outer product matrix (2) which is a part of the prior for global mixture weights ρ, change in the value of π k (1) changes values of the whole k-th row in the matrix. The model also represents different degrees of dependence by allowing any combination of cluster assignments across contexts. When there is a single common cluster structure across the two contexts, the occupied clusters will be concentrated along the diagonal of the probability matrix (2). For C > 2, the outer product of { π (c) } c = 1 C generalises into a tensor product. Each element of ρ represents the probability of a C-tuple of cluster assignments, i. e. a specific combination of cluster assignments in specific contexts. To summarise the model, we give a general formulation for C contexts. π (c) ∣ α 0 ∼ Dirichlet (α 0 K (c), …, α 0 K (c) ) ρ ∣ γ, { π (1), …, π (c) } ∼ Dirichlet (γ { ⊗ c = 1, …, C π (c) }) z n ∣ ρ ∼ Categorical (ρ), z n = (z n (1), …, z n (c) ) θ k (c) ∼ H (c), k = 1, …, K (c) x n (c) ∣ z n (c) = k ∼ F (c) (x n (c) | θ k (c) ) (3) Fig 22 shows the graphical model for the full context-dependent clustering model. In general, the number of clusters K (c) can be different for each context c. Given that the number of clusters in each dataset is O (K), the total length of ρ parameter vector is O (K C) because the model represents all possible combinations of cluster assignments. This yields a very large number of potential combinatorial clusters. However, only a small number of clusters is actually represented in the data and many of the clusters remain empty as shown by Rousseau et al. [19]. Also, by using small values for the concentration parameter γ < 1, we encourage the data to be concentrated in only a small number of global mixture components. This model explicitly represents all the possible combinations of cluster assignments, which may not be desirable in real-world applications, where the model may use some of the structure to capture technical noise present in the data. Also, although the number of clusters that are occupied is smaller in the posterior, the model size grows exponentially with the number of contexts. In the next section, we look at an alternative model that circumvents this limitation. To avoid the large number of potential cluster combinations in the previous model, we decouple the number of context-specific clusters K (c), c = 1, …, C, and the number of global clusters. First a mixture distribution over S global clusters is defined similarly to the finite Dirichlet mixture model [15]. ρ ∣ γ 0 ∼ Dirichlet (γ 0 S, …, γ 0 S) z n ∣ ρ ∼ Categorical (ρ) where ρ are the mixture weights and zn are standard cluster assignment indicator variables. We combine context-specific clusters with the global clusters using the following method: we specify context mixture distributions and a set of assignment variables k s (c) that associate global cluster s = 1, …, S with context-specific clusters: π (c) ∣ α 0 ∼ Dirichlet (α 0 K (c), …, α 0 K (c) ) k s (c) ∣ π (c) ∼ Categorical (π (c) ) (4) In this formulation, k s (c) ∈ { 1, …, K (c) } assigns the s-th global cluster to a specific local cluster in context c. One could say that (k s (c) ) s = 1 C are the coordinates of the global cluster in terms of the local cluster identifiers. The variables zn then assign samples to global clusters. This way, data are represented by a mixture of context-specific cluster combinations. In the previous model (3) the mapping of global clusters to local clusters was implicit, because each combination of context clusters mapped to a unique global cluster. In this model, the mapping is probabilistic and forms a part of the model. Note that compared to the previous model, we also have to specify the number of potential global clusters S which is no longer determined by the number of clusters within each context. This circumvents the problem of large dimensionality of the space of potential cluster combinations across contexts. To summarise, the complete model can be written as ρ ∣ γ 0 ∼ Dirichlet (γ 0 S, …, γ 0 S) z n ∣ ρ ∼ Categorical (ρ) π (c) ∣ α 0 ∼ Dirichlet (α 0 K (c), …, α 0 K (c) ) k s (c) ∣ π (c) ∼ Categorical (π (c) ) θ l (c) ∣ H (c) ∼ H (c) x n (c) ∣ z n, (k s (c) ) s = 1 S, (θ l) l = 1 K (c) ∼ F (c) (x n (c) ∣ θ k z n (c) ) (5) Fig 23 shows the graphical representation of this model. We derived Gibbs sampling inference algorithms for both formulations of the context-dependent clustering model, details and the inference equations can be found in S1 Appendix. For the fully combinatorial version of the model, computational complexity of each iteration of the Gibbs sampling algorithm is O (N C ∏ c = 1 C K (c) ), additionally multiplied by the complexity of evaluating the data likelihood F (c) for each context. We also derived approximate variational inference updates for this version of the model, which achieves faster convergence than Gibbs sampling. Details on variational inference in the model are also available in S1 Appendix. For the decoupled formulation of the model, the computational complexity of each Gibbs sampling iteration is O (N C S + S ∑ c = 1 C K (c) ), again additionally increased by the complexity of evaluating data likelihood F (c). Compared to the fully combinatorial model, complexity of this algorithm is lower for S < ∏ c = 1 C K (c) because of the decoupled representation. Implementation of the decoupled version of the context-dependent clustering model, which was used to produce the results presented in the Results section, is available as the R package clusternomics from CRAN.
To summarize, in this paper we proposed a clustering algorithm for integrative analysis of heterogeneous datasets. We described the probabilistic model behind the algorithm, which is closely related to the hierarchical Dirichlet process [20]. The proposed context-dependent clustering algorithm models both the local structure within each dataset, and the global structure which arises from combinations of dataset-specific clusters. This form of model enables modelling of heterogeneous related datasets that do not share the same structure. We described two representations of the model which are equivalent in their limit. The first full model makes the assumptions behind the model explicit and represents all possible combinations of context-specific cluster assignments. Given the number of clusters in each context K (c), the model is a mixture model over all possible combinations of cluster assignments in individual contexts. The second type of representation is the decoupled CDC model which allows us to specify the number of global clusters S that are identified in the data separately from the number of context-dependent clusters K (c). For a large number of global clusters S the model is equivalent to the full model. However, the number of global clusters allows us to additionally tune the resulting cluster structure. For smaller numbers of clusters S, the global and local cluster structures are forced to be more similar and the algorithm enforces a common cluster structure across all datasets. For larger numbers of global clusters, the model has more flexibility to model the local structure within each dataset. We evaluated the proposed model both on simulated data and on a set of real-world cancer datasets. The simulated data revealed that other algorithms for integrative clustering do not model situations where there is a varying degree of dependence of cluster structures across multiple datasets. We also evaluated the proposed context-dependent clustering model on the breast cancer dataset which includes four different contexts, and additionally on two datasets studying lung and kidney cancer. The model successfully identified clinically meaningful clusters as measured by the survival probabilities for each global cluster. We evaluated the decoupled clustering model over a number of possible global clusters. Generally, the best clustering results are obtained when the cluster structure stabilises as measured by the ARI. Senbabaoglu et al. [13] note that in real-world datasets there may be many different numbers of clusters that are equally highly plausible. The comparison of clustering results based on their agreement identifies the model sizes that lead to similar sets of cluster assignments. Each group then corresponds to an alternative interpretation of the data. Overall, the model uses different assumptions about the cluster structure than other currently used integrative clustering algorithms. By dropping the assumption of a single common cluster structure, the model identifies both the local structure of individual datasets, and a global structure that combines the local clusters.
Integrative clustering is the task of identifying groups of samples by combining information from several datasets. An example of this task is cancer subtyping, where we cluster tumour samples based on several datasets, such as gene expression, proteomics and others. Most existing algorithms assume that all such datasets share a similar cluster structure, with samples outside these clusters treated as noise. The structure can, however, be much more heterogeneous: some meaningful clusters may appear only in some datasets. In the paper, we introduce the Clusternomics algorithm that identifies groups of samples across heterogeneous datasets. It models both cluster structure of individual datasets, and the global structure that appears as a combination of local structures. The algorithm uses probabilistic modelling to identify the groups and share information across the local and global levels. We evaluated the algorithm on both simulated and real world datasets, where the algorithm found clinically significant clusters with different survival outcomes.
medicine and health sciences
breast tumors
gene regulation
applied mathematics
cancers and neoplasms
renal cancer
simulation and modeling
algorithms
oncology
micrornas
mathematics
clustering algorithms
epigenetics
dna
chromatin
dna methylation
research and analysis methods
chromosome biology
gene expression
breast cancer
chromatin modification
dna modification
biochemistry
rna
cell biology
nucleic acids
genetics
biology and life sciences
physical sciences
non-coding rna
nephrology
Clusternomics: Integrative context-dependent clustering for heterogeneous datasets