Hierarchical tree snipping: clustering guided by prior knowledge

Dotan-Cohen, Dikla; Melkman, Avraham A.; Kasif, Simon

doi:10.1093/bioinformatics/btm526

Cited by 22 publications

(20 citation statements)

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“…Our method improves the selection procedure by combining information on false discovery rate p-value with both functional group and characteristic temporal pattern association, thus diminishing the number of false negatives without significantly increasing the number of false positives. Previous works have addressed the integration of prior knowledge either in clustering algorithms [26]–[28] or in selection procedures [29], whereas our approach integrates selection, clustering and annotation in a single computational framework.…”

Section: Discussionmentioning

confidence: 99%

Function-Based Discovery of Significant Transcriptional Temporal Patterns in Insulin Stimulated Muscle Cells

et al. 2012

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BackgroundInsulin action on protein synthesis (translation of transcripts) and post-translational modifications, especially of those involving the reversible modifications such as phosphorylation of various signaling proteins, are extensively studied but insulin effect on transcription of genes, especially of transcriptional temporal patterns remains to be fully defined.Methodology/Principal FindingsTo identify significant transcriptional temporal patterns we utilized primary differentiated rat skeletal muscle myotubes which were treated with insulin and samples were collected every 20 min for 8 hours. Pooled samples at every hour were analyzed by gene array approach to measure transcript levels. The patterns of transcript levels were analyzed based on a novel method that integrates selection, clustering, and functional annotation to find the main temporal patterns associated to functional groups of differentially expressed genes. 326 genes were found to be differentially expressed in response to in vitro insulin administration in skeletal muscle myotubes. Approximately 20% of the genes that were differentially expressed were identified as belonging to the insulin signaling pathway. Characteristic transcriptional temporal patterns include: (a) a slow and gradual decrease in gene expression, (b) a gradual increase in gene expression reaching a peak at about 5 hours and then reaching a plateau or an initial decrease and other different variable pattern of increase in gene expression over time.Conclusion/SignificanceThe new method allows identifying characteristic dynamic responses to insulin stimulus, common to a number of genes and associated to the same functional group. The results demonstrate that insulin treatment elicited different clusters of gene transcript profile supporting a temporal regulation of gene expression by insulin in skeletal muscle cells.

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Section: Discussionmentioning

confidence: 99%

Function-Based Discovery of Significant Transcriptional Temporal Patterns in Insulin Stimulated Muscle Cells

et al. 2012

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“…The optimization function becomes maximizing for the interclass variance with the restriction that only allow those splits that can be explained by a common feature [17]. Dotan-Cohen et al proposed a hierarchical tree snipping method which incorporates the gene annotation information from Gene Ontology (GO) to obtain clusters that are substantially enriched in genes participating in the same biological process [18].…”

Section: Optimization Functionmentioning

confidence: 99%

Pattern recognition methods in microarray based oncology study

Lu¹,

Zhang

2009

Front. Electr. Electron. Eng. China

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With the development of microarray technology, more and more microarray-based oncology studies have been carried out. Huge amounts of data and the complexity of cancer mechanisms make data analysis methods a much more important part of these studies. In this article, we will mainly focus on the pattern recognition methods used in oncology studies. According to the availability of sample information, the unsupervised methods and supervised methods are reviewed separately. Finally, some possible future directions are proposed.

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“…Now, subtrees below these k edges define a partitioning into k classes. In general, however, there are numerous non-horizontal cuts supported by the same dendrogram that yield a different partitioning into the same number of clusters, which has been considered only recently in literature [23-25]. …”

Section: Introductionmentioning

confidence: 99%

Similarity maps and hierarchical clustering for annotating FT-IR spectral images

et al. 2013

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BackgroundUnsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization.ResultsWe introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy.ConclusionsWe demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward’s clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images.

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Hierarchical tree snipping: clustering guided by prior knowledge

Abstract: A java package is available at http://www.cs.bgu.ac.il/~dotna/ TreeSnipping

Cited by 22 publications

References 42 publications

Function-Based Discovery of Significant Transcriptional Temporal Patterns in Insulin Stimulated Muscle Cells

Function-Based Discovery of Significant Transcriptional Temporal Patterns in Insulin Stimulated Muscle Cells

Pattern recognition methods in microarray based oncology study

Similarity maps and hierarchical clustering for annotating FT-IR spectral images

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