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ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets.

Rydbeck H, Sandve GK, Ferkingstad E, Simovski B, Rye M, Hovig E - PLoS ONE (2015)

Bottom Line: We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks.An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface.The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics, University of Oslo, Oslo, Norway; Department of Tumour Biology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.

ABSTRACT
Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.

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Dendrogram of H3K4me1 track clustering, using the “Direct sequence-level similarity” for the genomic region chr1-22.All samples, except the ones from brain, are placed into separate subclusters per tissue. The clustering were performed according to the canonical case “Direct sequence-level similarity”. Each individual bp-location of the genome were considered an independent feature. The distance between two feature vectors was defined as ratio of the intersection to the union of base pairs covered by two feature vectors. Clustering was performed using standard hierarchical clustering with the average linkage criterion.
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pone.0123261.g001: Dendrogram of H3K4me1 track clustering, using the “Direct sequence-level similarity” for the genomic region chr1-22.All samples, except the ones from brain, are placed into separate subclusters per tissue. The clustering were performed according to the canonical case “Direct sequence-level similarity”. Each individual bp-location of the genome were considered an independent feature. The distance between two feature vectors was defined as ratio of the intersection to the union of base pairs covered by two feature vectors. Clustering was performed using standard hierarchical clustering with the average linkage criterion.

Mentions: The dendrogram obtained by the “Direct sequence-level similarity” case is shown in Fig 1. All sample pairs, except the one from brain, cluster together revealing that H3K4me1 tracks from samples from similar cell types overlap with each other more than they do with such tracks from other cell types. The samples from brain do, however, deviate from the rule, and cluster apart from each other, possibly reflecting that they originate from fetal and adult tissue, respectively. The dendrogram resulting from the larger number of tracks as specified in the batch script, is shown in Supplementary S1 Fig.


ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets.

Rydbeck H, Sandve GK, Ferkingstad E, Simovski B, Rye M, Hovig E - PLoS ONE (2015)

Dendrogram of H3K4me1 track clustering, using the “Direct sequence-level similarity” for the genomic region chr1-22.All samples, except the ones from brain, are placed into separate subclusters per tissue. The clustering were performed according to the canonical case “Direct sequence-level similarity”. Each individual bp-location of the genome were considered an independent feature. The distance between two feature vectors was defined as ratio of the intersection to the union of base pairs covered by two feature vectors. Clustering was performed using standard hierarchical clustering with the average linkage criterion.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4400084&req=5

pone.0123261.g001: Dendrogram of H3K4me1 track clustering, using the “Direct sequence-level similarity” for the genomic region chr1-22.All samples, except the ones from brain, are placed into separate subclusters per tissue. The clustering were performed according to the canonical case “Direct sequence-level similarity”. Each individual bp-location of the genome were considered an independent feature. The distance between two feature vectors was defined as ratio of the intersection to the union of base pairs covered by two feature vectors. Clustering was performed using standard hierarchical clustering with the average linkage criterion.
Mentions: The dendrogram obtained by the “Direct sequence-level similarity” case is shown in Fig 1. All sample pairs, except the one from brain, cluster together revealing that H3K4me1 tracks from samples from similar cell types overlap with each other more than they do with such tracks from other cell types. The samples from brain do, however, deviate from the rule, and cluster apart from each other, possibly reflecting that they originate from fetal and adult tissue, respectively. The dendrogram resulting from the larger number of tracks as specified in the batch script, is shown in Supplementary S1 Fig.

Bottom Line: We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks.An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface.The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics, University of Oslo, Oslo, Norway; Department of Tumour Biology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.

ABSTRACT
Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.

Show MeSH