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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
MarVis main window. The main window of MarVis after selecting a cluster/prototype for further analysis. The window is divided into several regions, which display different information: The prototype plot (compare with figure 1) shows the array of prototypes according to the current colormap (region 1a) and the number of marker candidates in the associated clusters (region 1b). The cluster plot (2) displays the intensity profiles of marker candidates in the currently activated cluster where a white rectangle marks the currently activated candidate and the associated prototype. The marker information box (3) shows detailed information of all candidates in the currently activated cluster. The marker scatter plot (4) displays the retention time vs. the mass-to-charge-ratio of each marker candidate in the currently activated cluster using big red dots. The currently activated candidate is represented by a big blue dot. In the background all marker candidates of the current data set are plotted as small gray dots. The active-prototype/marker plot (5) displays the magnified prototype profile of the activated cluster according to the current colormap.
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Figure 2: MarVis main window. The main window of MarVis after selecting a cluster/prototype for further analysis. The window is divided into several regions, which display different information: The prototype plot (compare with figure 1) shows the array of prototypes according to the current colormap (region 1a) and the number of marker candidates in the associated clusters (region 1b). The cluster plot (2) displays the intensity profiles of marker candidates in the currently activated cluster where a white rectangle marks the currently activated candidate and the associated prototype. The marker information box (3) shows detailed information of all candidates in the currently activated cluster. The marker scatter plot (4) displays the retention time vs. the mass-to-charge-ratio of each marker candidate in the currently activated cluster using big red dots. The currently activated candidate is represented by a big blue dot. In the background all marker candidates of the current data set are plotted as small gray dots. The active-prototype/marker plot (5) displays the magnified prototype profile of the activated cluster according to the current colormap.

Mentions: After selection of an appropriate clustering state for analysis, MarVis returns to the main window and displays the prototype profiles and the number of associated marker candidates in the upper right region. After mouse click on a column corresponding to a particular prototype, further information regarding this prototype is displayed in the other regions of the main window (see figure 2).


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)

MarVis main window. The main window of MarVis after selecting a cluster/prototype for further analysis. The window is divided into several regions, which display different information: The prototype plot (compare with figure 1) shows the array of prototypes according to the current colormap (region 1a) and the number of marker candidates in the associated clusters (region 1b). The cluster plot (2) displays the intensity profiles of marker candidates in the currently activated cluster where a white rectangle marks the currently activated candidate and the associated prototype. The marker information box (3) shows detailed information of all candidates in the currently activated cluster. The marker scatter plot (4) displays the retention time vs. the mass-to-charge-ratio of each marker candidate in the currently activated cluster using big red dots. The currently activated candidate is represented by a big blue dot. In the background all marker candidates of the current data set are plotted as small gray dots. The active-prototype/marker plot (5) displays the magnified prototype profile of the activated cluster according to the current colormap.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: MarVis main window. The main window of MarVis after selecting a cluster/prototype for further analysis. The window is divided into several regions, which display different information: The prototype plot (compare with figure 1) shows the array of prototypes according to the current colormap (region 1a) and the number of marker candidates in the associated clusters (region 1b). The cluster plot (2) displays the intensity profiles of marker candidates in the currently activated cluster where a white rectangle marks the currently activated candidate and the associated prototype. The marker information box (3) shows detailed information of all candidates in the currently activated cluster. The marker scatter plot (4) displays the retention time vs. the mass-to-charge-ratio of each marker candidate in the currently activated cluster using big red dots. The currently activated candidate is represented by a big blue dot. In the background all marker candidates of the current data set are plotted as small gray dots. The active-prototype/marker plot (5) displays the magnified prototype profile of the activated cluster according to the current colormap.
Mentions: After selection of an appropriate clustering state for analysis, MarVis returns to the main window and displays the prototype profiles and the number of associated marker candidates in the upper right region. After mouse click on a column corresponding to a particular prototype, further information regarding this prototype is displayed in the other regions of the main window (see figure 2).

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