Description Usage Arguments Details Value Author(s) References See Also Examples

`SCmapping`

identifies groups of clusters from two flat partitionings
that have the largest common intersections. These groups are found by
following a greedy strategy: all edges incident to each cluster are removed
except for the one(s) with highest weight; then the connected components in
the resulting bi-graph define the correspondences of superclusters.

1 2 3 |

`clustering1` |
a vector indicating the cluster in which each point is allocated in the first flat partitioning. |

`clustering2` |
a vector indicating the cluster in which each point is allocated in the second flat partitioning. |

`plotting` |
a Boolean parameter which leads to the representation of the bi-graph if TRUE. |

`h.min` |
the minimum separation between nodes in the same layer; if the barycentre algorithm sets two nodes to be less than this distance apart, then the second node and the following ones are shifted (downwards, in the vertical layout, and to the right, in the horizontal layout). |

`line.wd` |
a numerical parameter that fixes the width of the thickest edge, according to the weights; 3 by default. |

`point.sz` |
a numerical parameter that fixes the size of the nodes in the bi-graph; 2 by default. |

`offset` |
a numerical parameter that sets the separation between the nodes and their labels. It is set to 0.1 by default. |

`evenly` |
a Boolean parameter; if TRUE the coordinate values are ignored, and the nodes are drawn evenly spaced, according to the ordering obtained by the barycentre algorithm. It is set to FALSE by default. |

`horiz` |
a Boolean argument for vertical (default) or horizontal layout. |

`max.iter` |
an integer stating the maximum number of runs of the barycentre heuristic on both layers of the bi-graph. |

`node.col` |
defines the colour of nodes from both layers. |

`edge.col` |
sets the colour of the edges. |

`...` |
further graphical parameters can be passed to the function. |

The one-to-one mapping between groups of clusters from two different flat partitionings is computed with the greedy algorithm: firstly, for each node the edge with the highest weight is taken, and secondly, the connected components in the edge-reduced bi-graph are found, so that each connected component corresponds to a pair of superclusters with a large overlap.

a list containing:

`s.clustering1` |
a vector indicating the supercluster in which each point is allocated in the first superclustering. |

`s.clustering2` |
a vector indicating the supercluster in which each point is allocated in the second superclustering. |

`merging1` |
a list of p elements, whose j-th component contains the labels of the initial clusters from the first partitioning that have been merged to produce the j-th supercluster in the left layer of the bi- graph. |

`merging2` |
a list of p elements, whose j-th component contains the labels of the initial clusters from the second partitioning that have been merged to produce the j-th supercluster in the right layer of the bi- graph. |

`weights` |
a |

Aurora Torrente [email protected] and Alvis Brazma [email protected]

Torrente, A. *et al.* (2005). A new algorithm for comparing and
visualizing relationships between hierarchical and flat gene expression
data clusterings. *Bioinformatics*, 21 (21), 3993-3999.

barycentre, flatVSflat, flatVShier

1 2 3 4 5 |

Bioconductor-mirror/clustComp documentation built on July 28, 2017, 5:21 a.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.