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

`hbm`

builds a hierarchical block matrix from an association matrix, typically a symmetric chromatin contact map, by iteratively aggregating clusters.

1 |

`m` |
a numeric association matrix, typically a chromatin contact map. |

`infl` |
numeric giving the inflation parameter for |

`...` |
additional parameters for |

`hbm`

iteratively applies Markov Clustering (by calling `mcl`

). In the first iteration, clustering is applied on the input association matrix. The resulting clusters are used to generate a new association matrix to cluster, whose i,j-th entry gives the mean association between all the nodes in the i-th and j-th clusters found in the previous iteration. This is repeated until all clusters are aggregated to a single cluster or when clusters can no longer be aggregated together.

`hbm`

returns a list with the following objects:

`hm` |
The hierarchical block matrix, defined as: |

`scales` |
a list of length |

Yoli Shavit

`hbm`

's website: http://www.cl.cam.ac.uk/~ys388/hbm/

`mcl`

for the implementation of Markov Clustering

`detect.movement`

to see how `hbm`

's results are used to detect movements

`communicability`

to see how `hbm`

's results are used to compute the communicability between different locations.

`hierarchy`

to see how `hbm`

's results are used to compute the hierarchy of the association matrix.

`hbm`

's tutorials at http://www.cl.cam.ac.uk/~ys388/hbm/

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
set.seed(2)
n = 200 # chain size
# generate chain configuration (random walk/giant loop model)
conf = generate.random.conf(n, sd = 0.5, scale = FALSE)
# generate a contact map like matrix using the model c ~ exp(-d)
control = exp(-1*as.matrix(dist(conf)))
res = hbm(control)
m = res$hm
image(t(m)[,nrow(m):1], axes = FALSE)
ats = seq(0,1,0.2)
lbls = as.character(n*ats)
axis(1, at= ats, labels = lbls, cex.axis = 0.8)
ats = seq(1,0,-1*0.2)
lbls = as.character(n*seq(0,1,0.2))
axis(2, at= ats, labels = lbls, cex.axis = 0.8)
res$scales
``` |

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