Computational cluster validation in post-genomic data analysis

Handl, Julia; Knowles, Joshua; Kell, Douglas B.

doi:10.1093/bioinformatics/bti517

Cited by 801 publications

(670 citation statements)

References 59 publications

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“…A '1-eta 2 ' calculation was used as a distance measure between the profiles. The commonly chosen UPGMA (Unweighted Paired Group Method with Arithmetic mean) hierarchical clustering method (Eisen et al, 1998;Handl et al, 2005) sorted the eta 2 profiles for each seed's correlation map into three main groups ( Figure 4C), which recapitulates the anatomical ordering as well as the distinct shape differences seen in Figure 4A (blue curves vs green curves vs red curves). Further inspection of the clusters reveals two abrupt changes in the green curves (triangle and circle locations), which occurred at similar locations for the other curves, indicating functional transitions that are candidates for areal boundaries.…”

Section: Resultsmentioning

confidence: 95%

Defining functional areas in individual human brains using resting functional connectivity MRI

et al. 2008

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The cerebral cortex is anatomically organized at many physical scales starting at the level of single neurons and extending up to functional systems. Current functional magnetic resonance imaging (fMRI) studies often focus at the level of areas, networks, and systems. Except in restricted domains, (e.g. topographically-organized sensory regions), it is difficult to determine area boundaries in the human brain using fMRI. The ability to delineate functional areas non-invasively would enhance the quality of many experimental analyses allowing more accurate across-subject comparisons of independently identified functional areas. Correlations in spontaneous BOLD activity, often referred to as resting state functional connectivity (rs-fcMRI), are especially promising as a way to accurately localize differences in patterns of correlated activity across large expanses of cortex. In the current report, we applied a novel set of image analysis tools to explore the utility of rs-fcMRI for defining wide-ranging functional area boundaries. We find that rs-fcMRI patterns show sharp transitions in correlation patterns and that these putative areal boundaries can be reliably detected in individual subjects as well as in group data. Additionally, combining surface-based analysis techniques with image processing algorithms allows automated mapping of putative areal boundaries across large expanses of cortex without the need for prior information about a region's function or topography. Our approach reliably produces maps of bounded regions appropriate in size and number for putative functional areas. These findings will hopefully stimulate further methodological refinements and validations.CORRESPONDING AUTHORS: Alexander

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Section: Resultsmentioning

confidence: 95%

Defining functional areas in individual human brains using resting functional connectivity MRI

et al. 2008

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“…It is important to make clear, that the use of relative indexes (such as the Silhouette) is just part of the more general procedure that comprehends the whole clustering analysis, i.e., (i) pre-processing, (ii) clustering and, (iii) validation [72]. To this extent, in a real application, relative indexes may, in turn, help the user to choose the "best" partition or the "best" number of clusters for a given dataset (according to the criterion).…”

Section: Methodsmentioning

confidence: 99%

On the selection of appropriate distances for gene expression data clustering

2014

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BackgroundClustering is crucial for gene expression data analysis. As an unsupervised exploratory procedure its results can help researchers to gain insights and formulate new hypothesis about biological data from microarrays. Given different settings of microarray experiments, clustering proves itself as a versatile exploratory tool. It can help to unveil new cancer subtypes or to identify groups of genes that respond similarly to a specific experimental condition. In order to obtain useful clustering results, however, different parameters of the clustering procedure must be properly tuned. Besides the selection of the clustering method itself, determining which distance is going to be employed between data objects is probably one of the most difficult decisions.Results and conclusionsWe analyze how different distances and clustering methods interact regarding their ability to cluster gene expression, i.e., microarray data. We study 15 distances along with four common clustering methods from the literature on a total of 52 gene expression microarray datasets. Distances are evaluated on a number of different scenarios including clustering of cancer tissues and genes from short time-series expression data, the two main clustering applications in gene expression. Our results support that the selection of an appropriate distance depends on the scenario in hand. Moreover, in each scenario, given the very same clustering method, significant differences in quality may arise from the selection of distinct distance measures. In fact, the selection of an appropriate distance measure can make the difference between meaningful and poor clustering outcomes, even for a suitable clustering method.

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“…Instead, we seek to identify clusters that are both highly correlated and spatially distant, which could not be identified from molecular structure or geometry alone. To achieve this, N clusters are formed by choosing the number of clusters (N) at the knee point of a clustering performance curve that shows compactness (intracluster variance) and separation (inter-cluster partitioning) of resulting clusters as a function of the number of clusters [52]. Compactness is computed as the mean cluster size, where cluster size is defined as the mean pair-wise distance between all elements (FE nodes) in the cluster.…”

Section: Correlations In Molecular Motionsmentioning

confidence: 99%

Conformational dynamics of supramolecular protein assemblies

Kim

Nguyen

Bathe

2011

Journal of Structural Biology

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Supramolecular protein assemblies including molecular motors, cytoskeletal filaments, chaperones, and ribosomes play a central role in a broad array of cellular functions ranging from cell division and motility to RNA and protein synthesis and folding. Single-particle reconstructions of such assemblies have been growing rapidly in recent years, providing increasingly high resolution structural information under native conditions. While the static structure of these assemblies provides essential insight into their mechanism of biological function, their dynamical motions provide additional important information that cannot be inferred from structure alone. Here we present an unsupervised computational framework for the analysis of high molecular weight assemblies and use it to analyze the conformational dynamics of structures deposited in the Electron Microscopy Data Bank. Protein assemblies are modeled using a recently introduced coarse-grained modeling framework based on the finite element method, which is used to compute equilibrium thermal fluctuations, elastic strain energy distributions associated with specific conformational transitions, and dynamical correlations in distant molecular domains. Results are presented in detail for the ribosome-bound termination factor RF2 from Escherichia coli, the nuclear pore complex from Dictyostelium discoideum, and the chaperonin GroEL from E. coli. Elastic strain energy distributions reveal hinge-regions associated with specific conformational change pathways, and correlations in collective molecular motions reveal dynamical coupling between distant molecular domains that suggest new, as well as confirm existing, allosteric mechanisms. Results are publically available for use in further investigation and interpretation of biological function including cooperative transitions, allosteric communication, and molecular mechanics, as well as in further classification and refinement of electron microscopy based structures.

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Computational cluster validation in post-genomic data analysis

Abstract: Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.

Cited by 801 publications

References 59 publications

Defining functional areas in individual human brains using resting functional connectivity MRI

Defining functional areas in individual human brains using resting functional connectivity MRI

On the selection of appropriate distances for gene expression data clustering

Conformational dynamics of supramolecular protein assemblies

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