In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package "UNCLES" available on http://cran.r-project.org/web/packages/UNCLES/index.html .

In this paper, we propose a new consensus clustering algorithm, which is based on an existing clustering paradigm, called enhanced splitting merging awareness tactics (E-SMART). The problem of determining the number of clusters, which affects many state-of-theart consensus clustering algorithms, is addressed by the proposed CoCE-SMART algorithm. The idea behind CoCE-SMART is that SMART is used repeatedly to one dataset, resulting in different clustering results, which might have different numbers of clusters. These SMART clustering results can be combined by clustering the centroids of all clusters as the estimate of real number of clusters can be determined from the SMART clustering results. Three benchmark datasets are utilised to assess the proposed algorithm. The experimental results strongly indicate that the proposed CoCE-SMART algorithm outperforms other state-of-the-art consensus clustering algorithms.

In this paper, we propose a scalable clustering paradigm to address the problems of excessive computational load and limited clustering performance in large-scale data. The proposed method employs the enhanced splitting merging awareness tactics (E-SMART) algorithm. The large-scale dataset is divided into many sub-datasets sampled randomly from original data. These sub-datasets are clustered using E-SMART with the number of clusters K detected automatically and the resulting partitions are combined and re-clustered. We evaluate our method using synthetic fMRI datasets with different noise levels and one real fMRI dataset. Results show that the accuracy and execution time outperforms the traditional clustering algorithms in large-scale datasets.

BACKGROUND: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. RESULTS: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. CONCLUSIONS: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

The recently proposed UNCLES method has the ability to unify clustering results from multiple datasets under different types of external specifications. It can also tunably tighten the results such that many objects are unassigned from all of the clusters to obtain few tight clusters. Despite the success of this method, setting its parameters, such as the number of clusters (K) and the tuning parameters δ and (δ+, δ-), has never been automated. As its clusters vary in size, they cannot be validated by the existing validation indices. In this study we present a technique of validation based on our proposed M-N scatter plots. This technique has the ability to provide better fitness values for the clusters which include more objects while preserving their tightness. This well suits the nature of the results of UNCLES. We have applied this technique to a set of bacterial microarray datasets as well as a set of English vowels datasets. Our results demonstrate the success of the M-N plots in selecting the best few clusters out of a pool of clusters generated under varying K, δ, and (δ+, δ-) values. Our results also show that the best few clusters can be originated from different partitions, which shows the power of our technique in evaluating individual clusters rather than whole partitions. Finally, despite proposing this technique within the context of the UNCLES framework, it is readily applicable to other clustering results, especially when the parameters are not confidently predefined.

To meet the demand of clustering high dimensional data efficiently, in this paper, we propose a component-wise expectation conditional maximisation (CW-ECM) algorithm and integrate it within the recent proposed splitting-while-merging framework, which is called splitting-merging awareness tactics (SMART), for the mixture of factor analysers (MFA) model. The new algorithm has two advantages: it has ability to converge to actual or close actual number of clusters by a splitting-while-merging strategy, and it avoids the local maxima effectively and efficiently. Furthermore, we improve the splitting strategy in the original SMART framework and save more computational effort. We test out algorithm in two benchmark datasets and compare it with the state-of-the-art algorithms using many validation metrics. The results show that the proposed algorithm outperforms the compared algorithms in clustering performance with significantly less computational complexity.

BACKGROUND: The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result. RESULTS: In this paper we have performed comprehensive analysis of forty yeast microarray datasets. One recently described Bi-CoPaM method can analyse expressions of the same set of genes from various microarray datasets while using different clustering methods, and then combine these results into a single consensus result whose clusters' tightness is tunable from tight, specific clusters to wide, overlapping clusters. This has been adopted in a novel way over genome-wide data from forty yeast microarray datasets to discover two clusters of genes that are consistently co-expressed over all of these datasets from different biological contexts and various experimental conditions. Most strikingly, average expression profiles of those clusters are consistently negatively correlated in all of the forty datasets while neither profile leads or lags the other. CONCLUSIONS: The first cluster is enriched with ribosomal biogenesis genes. The biological processes of most of the genes in the second cluster are either unknown or apparently unrelated although they show high connectivity in protein-protein and genetic interaction networks. Therefore, it is possible that this mostly uncharacterised cluster and the ribosomal biogenesis cluster are transcriptionally oppositely regulated by some common machinery. Moreover, we anticipate that the genes included in this previously unknown cluster participate in generic, in contrast to specific, stress response processes. These novel findings illuminate coordinated gene expression in yeast and suggest several hypotheses for future experimental functional work. Additionally, we have demonstrated the usefulness of the Bi-CoPaM-based approach, which may be helpful for the analysis of other groups of (microarray) datasets from other species and systems for the exploration of global genetic co-expression.

Many methods have been proposed to identify informative subsets of genes in microarray studies in order to focus the research. For instance, the recently proposed binarization of consensus partition matrices (Bi-CoPaM) method has, amongst its various features, the ability to generate tight clusters of genes while leaving many genes unassigned from all clusters. We propose exploiting this particular feature by applying the Bi-CoPaM over genome-wide microarray data from multiple datasets to generate more clusters than required. Then, these clusters are tightened so that most of their genes are left unassigned from all clusters, and most of the clusters are left totally empty. The tightened clusters, which are still not empty, include those genes that are consistently co-expressed in multiple datasets when examined by various clustering methods. An example of this is demonstrated in this paper for cyclic and acyclic genes as well as for genes that are highly expressed and that are not. Thus, the results of our proposed approach cannot be reproduced by other methods of genes' periodicity identification or by other methods of clustering.

This study proposes a novel approach for the analysis of brain responses in the modality of ongoing EEG elicited by the naturalistic and continuous music stimulus. The 512-second long EEG data (recorded with 64 electrodes) are first decomposed into 64 components by independent component analysis (ICA) for each participant. Then, the spatial maps showing dipolar brain activity are selected in terms of the residual dipole variance through a single dipole model in brain imaging, and clustered into a pre-defined number (estimated by the minimum description length) of clusters. Subsequently, the temporal courses of the EEG theta and alpha oscillations of each component for each cluster are produced and correlated with the temporal courses of tonal and rhythmic features of the music. Using this approach, we found that the extracted temporal courses of the theta and alpha oscillations along central and occipital area of scalp in two of the selected clusters significantly correlated with the musical features representing progressions in the rhythmic content of the stimulus. We suggest that this demonstrates that with the proposed approach, we have managed to discover what kinds of brain responses were elicited when a participant was listening continuously to the long piece of naturalistic music.