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## neighborhood graphs

### Citations

4463 | Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, - Development - 2008 |

2895 | Cluster analysis and display of genome-wide expression patterns”
- Eisen, Spellman, et al.
- 1998
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Citation Context ...n understanding of the relationships between segments of a partition and make it easier to interpret the cluster results. In hierarchical clustering dendrograms and heatmaps are routinely used (e.g., =-=Eisen et al., 1998-=-). The most popular group of partitioning cluster algorithms are centroid–based cluster algorithms (e.g., K–means or Partitioning Around Medoids). Once a set of centroids has been found centroid–based... |

774 | Limma: linear models for microarray data,”
- Smyth
- 2005
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Citation Context ...e data were preprocessed using print–tip loess normalization. Differential expression estimates were calculated using Bioconductor (Gentleman et al., 2005, http://www.bioconductor.org) package limma (=-=Smyth, 2005-=-). The two data sets were filtered by selecting genes with p-value of the corresponding F-statistic smaller 0.05. Additionally, only genes expressed at a certain level (average log intensity A larger ... |

511 |
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.
- Rousseeuw
- 1987
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Citation Context ...Once a set of centroids has been found centroid–based cluster solutions are usually visualized by projection of the data into two dimensions (e.g., by principal component analysis). Silhouette plots (=-=Rousseeuw, 1987-=-) can be used to check whether clusters of points are well separated whereas topology representing networks (Martinetz and Schulten, 1994) reveal similarity between clusters. Neighborhood graphs (Leis... |

316 | Exploring expression data: identification and analysis of coexpressed genes. Genome Res.
- LJ, Kruglyak, et al.
- 1999
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Citation Context ... visualization methods discussed in this paper are designed for cluster solutions of partitioning cluster algorithms where clusters can be represented by centroids (e.g., K–means and PAM or QT–Clust (=-=Heyer et al., 1999-=-)). Neighborhood graphs Neighborhood graphs (Leisch, 2006) use the mean relative distances between points and centers as edge weights in order to measure how separated pairs of clusters are. Hence the... |

250 | NCBI GEO: mining tens of millions of expression profiles—database and tools update,”Nucleic - Barrett, Troup, et al. - 2007 |

212 |
Topology representing networks,
- Martinetz, Schulten
- 1994
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Citation Context ...o two dimensions (e.g., by principal component analysis). Silhouette plots (Rousseeuw, 1987) can be used to check whether clusters of points are well separated whereas topology representing networks (=-=Martinetz and Schulten, 1994-=-) reveal similarity between clusters. Neighborhood graphs (Leisch, 2006) combine these two approaches to visualize cluster structure. In this paper recent extensions of R package gcExplorer (Scharl an... |

129 |
RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organisation, and growth conditions.
- Salgado, Gama-Castro, et al.
- 2006
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Citation Context ...sources of external knowledge are the GenProtEC (Serres et al., 2004, http://genprotec.mbl.edu/) classification system for cellular and physiological roles of E. coli gene products and the RegulonDB (=-=Salgado et al., 2006-=-, http://regulondb.ccg.unam.mx/) for detailed information about operons and regulons. Software and implementation All cluster algorithms and visualization methods used are implemented in the statistic... |

117 | Integrating high-throughput and computational data elucidates bacterial networks." - Covert, Knight - 2004 |

47 | M (2004) GenProtEC: an updated and improved analysis of functions of Escherichia coli K-12 proteins. Nucleic Acids Res 32: D300–302 - MH, Goswami, et al. |

43 | Toolbox for K-Centroids Cluster Analysis. Computational statistics and data analysis, - Leisch - 2006 |

34 | Techniques for clustering gene expression data. Computers in biology and medicine - Kerr, Ruskin, et al. - 2008 |

25 | Reverse engineering molecular regulatory networks from microarray data with qp-graphs, - Castelo, Roverato - 2009 |

23 | Analysis of time-series gene expression data: methods, challenges, and opportunities - Androulakis, Yang, et al. |

10 | gcExplorer: interactive exploration of gene clusters - Scharl, Leisch - 2009 |

8 | The stochastic QT–Clust algorithm: evaluation of stability and variance on time–course microarray data
- Scharl, Leisch
- 2006
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Citation Context ...nd Additional file 2 for the corresponding R code). Exploratory analysis Now the PS19 data is used to demonstrate the new functionality of gcExplorer. The data is clustered using stochastic QT–Clust (=-=Scharl and Leisch, 2006-=-) yielding a cluster object which consists of 14 clusters. The neighborhood graph of the cluster solution shown in Figure 1 allows a detailed view on the cluster structure even for a large number of c... |

7 |
Gentry J: Bioconductor Software for Graphs
- VJ, Gentleman, et al.
- 2005
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Citation Context ...ar layout algorithms implemented in the open source graph visualization software Graphviz (http://www.graphviz.org/) for the display of neighborhood graphs. Bioconductor packages graph and Rgraphviz (=-=Carey et al., 2005-=-) provide tools for creating, manipulating, and visualizing graphs in R as well as an interface to Graphviz. Rgraphviz returns the layout information for a graph object, x- and y–coordinates of the gr... |

5 | Advances in cluster analysis of microarray data - Sheng, Moreau, et al. - 2005 |

5 | Tuning the transcription rate of recombinant protein in strong Escherichia coli expression systems through repressor titration, - Striedner, Cserjan-Puschmann, et al. - 2003 |

4 | F: Visualizing Gene Clusters Using Neighborhood Graphs in R
- Scharl, Leisch
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Citation Context ...directly interpretable. However, LDA does not scale well in the number of clusters, and relationships between the centroids of more than 15 clusters can hardly be displayed in the plane. As shown in (=-=Scharl and Leisch, 2008-=-) linear methods cannot be used for high–dimensional gene expression data and a large number of clusters. R package gcExplorer (Scharl and Leisch, 2009) uses non–linear layout algorithms implemented i... |

2 | R Graphics. Chapman & Hall/CRC Computer Science & Data Analysis - Murrell - 2005 |

1 |
Visualization of Centroid–Based Cluster Solutions
- Voglhuber
- 2008
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Citation Context ...adding further information to the display of the neighborhood graph is to use different graphical symbols for the representation of nodes. For that purpose gcExplorer makes use of R package symbols ((=-=Voglhuber, 2008-=-), http://r-forge.r-project.org/projects/ symbols). symbols is based on Grid (Murrell, August 2005), a very flexible graphics system for R. Grid features viewports, i.e., rectangular areas allowing th... |