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MarVis: a tool for clustering and visualization of metabolic biomarkers.

Kaever A, Lingner T, Feussner K, Göbel C, Feussner I, Meinicke P - BMC Bioinformatics (2009)

Bottom Line: The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data.The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown.Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. alex@gobics.de

ABSTRACT

Background: A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.

Results: We present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data.

Conclusion: MarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.

Show MeSH
Clustering progress window (final state). The clustering progress window after completion of the clustering process. The scrollbar in the middle of the window may be used to browse through intermediate clustering results with a higher smoothing over the prototype profiles. In the upper plot, MarVis displays the prototypes of the selected clustering state according to the current colormap. By default, MarVis uses the Jet colormap, i.e. red colors represent high intensities and blue colors represent low intensities. The vertical axis represents the different data set conditions, the horizontal axis corresponds to the prototype numbers. In the lower plot, the number of marker candidates that are associated with each prototype are represented as vertical bars with different height.
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Figure 1: Clustering progress window (final state). The clustering progress window after completion of the clustering process. The scrollbar in the middle of the window may be used to browse through intermediate clustering results with a higher smoothing over the prototype profiles. In the upper plot, MarVis displays the prototypes of the selected clustering state according to the current colormap. By default, MarVis uses the Jet colormap, i.e. red colors represent high intensities and blue colors represent low intensities. The vertical axis represents the different data set conditions, the horizontal axis corresponds to the prototype numbers. In the lower plot, the number of marker candidates that are associated with each prototype are represented as vertical bars with different height.

Mentions: After confirmation of the import options, the Clustering dialog is opened. Here, a title (dataset1.csv) and the number of prototypes (e.g. 50) have to be specified. MarVis starts the iterative clustering process and displays the intermediate prototype intensity profiles for each clustering state and the number of currently associated marker candidates in a separate window. The clustering process for the example data set only takes a few seconds on a standard PC. After the clustering process has been finished, the clustering state can be selected according to the desired degree of prototype smoothing (see [11]) by adjusting a scrollbar (see figure 1). Here, we choose the final clustering state corresponding to a minimal amount of smoothing, which is suitable for analysis in most cases.


MarVis: a tool for clustering and visualization of metabolic biomarkers.

Kaever A, Lingner T, Feussner K, Göbel C, Feussner I, Meinicke P - BMC Bioinformatics (2009)

Clustering progress window (final state). The clustering progress window after completion of the clustering process. The scrollbar in the middle of the window may be used to browse through intermediate clustering results with a higher smoothing over the prototype profiles. In the upper plot, MarVis displays the prototypes of the selected clustering state according to the current colormap. By default, MarVis uses the Jet colormap, i.e. red colors represent high intensities and blue colors represent low intensities. The vertical axis represents the different data set conditions, the horizontal axis corresponds to the prototype numbers. In the lower plot, the number of marker candidates that are associated with each prototype are represented as vertical bars with different height.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2666665&req=5

Figure 1: Clustering progress window (final state). The clustering progress window after completion of the clustering process. The scrollbar in the middle of the window may be used to browse through intermediate clustering results with a higher smoothing over the prototype profiles. In the upper plot, MarVis displays the prototypes of the selected clustering state according to the current colormap. By default, MarVis uses the Jet colormap, i.e. red colors represent high intensities and blue colors represent low intensities. The vertical axis represents the different data set conditions, the horizontal axis corresponds to the prototype numbers. In the lower plot, the number of marker candidates that are associated with each prototype are represented as vertical bars with different height.
Mentions: After confirmation of the import options, the Clustering dialog is opened. Here, a title (dataset1.csv) and the number of prototypes (e.g. 50) have to be specified. MarVis starts the iterative clustering process and displays the intermediate prototype intensity profiles for each clustering state and the number of currently associated marker candidates in a separate window. The clustering process for the example data set only takes a few seconds on a standard PC. After the clustering process has been finished, the clustering state can be selected according to the desired degree of prototype smoothing (see [11]) by adjusting a scrollbar (see figure 1). Here, we choose the final clustering state corresponding to a minimal amount of smoothing, which is suitable for analysis in most cases.

Bottom Line: The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data.The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown.Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. alex@gobics.de

ABSTRACT

Background: A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.

Results: We present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data.

Conclusion: MarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.

Show MeSH