3 research outputs found
Clustering consistency in neuroimaging data analysis
Clustering techniques have been applied to neuroscience data analysis for decades. New algorithms keep being developed and applied to address different problems. However, when it comes to the applications of clustering, it is often hard to select the appropriate algorithm and evaluate the quality of clustering results due to the unknown ground truth. It is also the case that conclusions might be biased based on only one specific algorithm because each algorithm has its own assumption of the structure of the data, which might not be the same as the real data. In this paper, we explore the benefits of integrating the clustering results from multiple clustering algorithms by a tunable consensus clustering strategy and demonstrate the importance and necessity of consistency in neuroimaging data analysis
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Consensus clustering framework for analysing fMRI datasets.
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNeuroimaging of humans has gained a position of status within neuroscience. Modern functional
magnetic resonance imaging (fMRI) technique provides neuroscientists with a powerful tool to
depict the complex architecture of human brains. fMRI generates large amount of data and many
analysis methods have been proposed to extract useful information from the data. Clustering
technique has been one of the most popular data-driven techniques to study brain functional connectivity,
which excels when traditional model-based approaches are difficult to implement. However,
the reliability and consistency of many findings are jeopardised by too many analysis methods,
parameters, and sometimes too few samples used. In this thesis, a consensus clustering
analysis framework for analysing fMRI data has been developed, aiming at overcoming the clustering
algorithm selection problem as well as reliability issues in neuroimaging. The framework is
able to identify groups of voxels representing brain regions that consistently exhibiting correlated
BOLD activities across many experimental conditions by integrating clustering results from multiple
clustering algorithms and various parameters such as the number of clusters . In the framework,
the individual clustering result generation is aided by high performance grid computing technique
to reduce the overall computational time. The integration of clustering results is implemented
by a technique named binarisation of consensus partition matrix (Bi-CoPaM) adapted and
enhanced for fMRI data analysis. The whole framework has been validated and is robust to participants’
individual variability, yielding most complete and reproducible clusters compared to the
traditional single clustering approach. This framework has been applied to two real fMRI studies
that investigate brain responses to listening to the emotional music with different preferences. In
the first fMRI study, three brain structures related to visual, reward, and auditory processing are
found to have intrinsic temporal patterns of coherent neuroactivity during affective processing,
which is one of the few data-driven studies that have observed. In the second study, different
levels of engagement, i.e. intentional to unintentional, with music have unique effects on the auditory-
limbic connectivity when listening to music, which has not been investigated and understood well in euro science of music field. We believe the work in this thesis has demonstrated an effective and competent approach to address the reliability and consistency concerns in fMRI data analysis
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Collective analysis of multiple high-throughput gene expression datasets
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonModern technologies have resulted in the production of numerous high-throughput biological datasets. However, the pace of development of capable computational methods does not cope with the pace of generation of new high-throughput datasets. Amongst the most popular biological high-throughput datasets are gene expression datasets (e.g. microarray datasets). This work targets this aspect by proposing a suite of computational methods which can analyse multiple gene expression datasets collectively. The focal method in this suite is the unification of clustering results from multiple datasets using external specifications (UNCLES). This method applies clustering to multiple heterogeneous datasets which measure the expression of the same set of genes separately and then combines the resulting partitions in accordance to one of two types of external specifications; type A identifies the subsets of genes that are consistently co-expressed in all of the given datasets while type B identifies the subsets of genes that are consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets. This contributes to the types of questions which can addressed by computational methods because existing clustering, consensus clustering, and biclustering methods are inapplicable to address the aforementioned objectives. Moreover, in order to assist in setting some of the parameters required by UNCLES, the M-N scatter plots technique is proposed. These methods, and less mature versions of them, have been validated and applied to numerous real datasets from the biological contexts of budding yeast, bacteria, human red blood cells, and malaria. While collaborating with biologists, these applications have led to various biological insights. In yeast, the role of the poorly-understood gene CMR1 in the yeast cell-cycle has been further elucidated. Also, a novel subset of poorly understood yeast genes has been discovered with an expression profile consistently negatively correlated with the well-known ribosome biogenesis genes. Bacterial data analysis has identified two clusters of negatively correlated genes. Analysis of data from human red blood cells has produced some hypotheses regarding the regulation of the pathways producing such cells. On the other hand, malarial data analysis is still at a preliminary stage. Taken together, this thesis provides an original integrative suite of computational methods which scrutinise multiple gene expression datasets collectively to address previously unresolved questions, and provides the results and findings of many applications of these methods to real biological datasets from multiple contexts.National Institute for Health Research (NIHR) and the Brunel College of Engineering, Design and Physical Science