hierarchical_partition: Hierarchical partition
In cola: A Framework for Consensus Partitioning

Description Usage Arguments Details Value Author(s) Examples

Hierarchical partition

hierarchical_partition(data,
    top_value_method = "ATC",
    partition_method = "skmeans",
    PAC_cutoff = 0.2, min_samples = 6, subset = Inf,
    max_k = 4, verbose = TRUE, mc.cores = 1, ...)

`data`	a numeric matrix where subgroups are found by columns.
`top_value_method`	a single top-value method. Available methods are in `all_top_value_methods`.
`partition_method`	a single partition method. Available methods are in `all_partition_methods`.
`PAC_cutoff`	the cutoff of PAC scores to determine whether to continue looking for subgroups.
`min_samples`	the cutoff of number of samples to determine whether to continue looking for subgroups.
`subset`	Number of columns to randomly sample.
`max_k`	maximal number of partitions to try. The function will try `2:max_k` partitions. Note this is the number of partitions that will be tried out on each node of the hierarchical partition. Since more subgroups will be found in the whole partition hierarchy, on each node, `max_k` should not be set to a large value.
`verbose`	whether print message.
`mc.cores`	multiple cores to use.
`...`	pass to `consensus_partition`

The function looks for subgroups in a hierarchical way.

There is a special way to encode the node in the hierarchy. The length of the node name is the depth of the node in the hierarchy and the substring excluding the last digit is the name node of the parent node. E.g. for the node 0011, the depth is 4 and the parent node is 001.

A HierarchicalPartition-class object. Simply type object in the interactive R session to see which functions can be applied on it.

Zuguang Gu <z.gu@dkfz.de>