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A visual analytics approach for models of heterogeneous cell populations.

Hasenauer J, Heinrich J, Doszczak M, Scheurich P, Weiskopf D, Allgöwer F - EURASIP J Bioinform Syst Biol (2012)

Bottom Line: We propose an analysis based on visual analytics to tackle this problem.The method can be employed to study qualitative and quantitative differences among cells.To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany. jan.hasenauer@ist.uni-stuttgart.de.

ABSTRACT
In recent years, cell population models have become increasingly common. In contrast to classic single cell models, population models allow for the study of cell-to-cell variability, a crucial phenomenon in most populations of primary cells, cancer cells, and stem cells. Unfortunately, tools for in-depth analysis of population models are still missing. This problem originates from the complexity of population models. Particularly important are methods to determine the source of heterogeneity (e.g., genetics or epigenetic differences) and to select potential (bio-)markers. We propose an analysis based on visual analytics to tackle this problem. Our approach combines parallel-coordinates plots, used for a visual assessment of the high-dimensional dependencies, and nonlinear support vector machines, for the quantification of effects. The method can be employed to study qualitative and quantitative differences among cells. To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.

No MeSH data available.


Related in: MedlinePlus

Parallel-coordinates plots represent multi-dimensional data as polylines crossing parallel axes. A point in Cartesian coordinates is mapped to a line in parallel-coordinates. As more axes are added, a line can be traced over all dimensions, which greatly facilitates the perception of multi-dimensional data.
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Figure 2: Parallel-coordinates plots represent multi-dimensional data as polylines crossing parallel axes. A point in Cartesian coordinates is mapped to a line in parallel-coordinates. As more axes are added, a line can be traced over all dimensions, which greatly facilitates the perception of multi-dimensional data.

Mentions: Parallel-coordinates [17] are a popular visualization technique for high-dimensional data. A parallel-coordinates plot is constructed by placing axes in parallel, as illustrated in Figure 2. A single pair of adjacent axes represents a 2-D projection of the data, where a point of the corresponding Cartesian coordinates is mapped to a line in parallel-coordinates, and vice versa. Due to this point-line duality, the same patterns emerge in a parallel-coordinates plot as in the dual Cartesian coordinates. However, adding more axes not only allows to visualize a set of pairwise relations, but also supports the viewer in tracing lines over all dimensions. As a result, multi-dimensional outliers and clusters can be visualized together with 2-D relations and the distribution of values for single dimensions.


A visual analytics approach for models of heterogeneous cell populations.

Hasenauer J, Heinrich J, Doszczak M, Scheurich P, Weiskopf D, Allgöwer F - EURASIP J Bioinform Syst Biol (2012)

Parallel-coordinates plots represent multi-dimensional data as polylines crossing parallel axes. A point in Cartesian coordinates is mapped to a line in parallel-coordinates. As more axes are added, a line can be traced over all dimensions, which greatly facilitates the perception of multi-dimensional data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3403928&req=5

Figure 2: Parallel-coordinates plots represent multi-dimensional data as polylines crossing parallel axes. A point in Cartesian coordinates is mapped to a line in parallel-coordinates. As more axes are added, a line can be traced over all dimensions, which greatly facilitates the perception of multi-dimensional data.
Mentions: Parallel-coordinates [17] are a popular visualization technique for high-dimensional data. A parallel-coordinates plot is constructed by placing axes in parallel, as illustrated in Figure 2. A single pair of adjacent axes represents a 2-D projection of the data, where a point of the corresponding Cartesian coordinates is mapped to a line in parallel-coordinates, and vice versa. Due to this point-line duality, the same patterns emerge in a parallel-coordinates plot as in the dual Cartesian coordinates. However, adding more axes not only allows to visualize a set of pairwise relations, but also supports the viewer in tracing lines over all dimensions. As a result, multi-dimensional outliers and clusters can be visualized together with 2-D relations and the distribution of values for single dimensions.

Bottom Line: We propose an analysis based on visual analytics to tackle this problem.The method can be employed to study qualitative and quantitative differences among cells.To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany. jan.hasenauer@ist.uni-stuttgart.de.

ABSTRACT
In recent years, cell population models have become increasingly common. In contrast to classic single cell models, population models allow for the study of cell-to-cell variability, a crucial phenomenon in most populations of primary cells, cancer cells, and stem cells. Unfortunately, tools for in-depth analysis of population models are still missing. This problem originates from the complexity of population models. Particularly important are methods to determine the source of heterogeneity (e.g., genetics or epigenetic differences) and to select potential (bio-)markers. We propose an analysis based on visual analytics to tackle this problem. Our approach combines parallel-coordinates plots, used for a visual assessment of the high-dimensional dependencies, and nonlinear support vector machines, for the quantification of effects. The method can be employed to study qualitative and quantitative differences among cells. To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.

No MeSH data available.


Related in: MedlinePlus