hierarchical_partition {cola}R Documentation

Hierarchical partition

Description

Hierarchical partition

Usage

hierarchical_partition(data, top_value_method = "MAD", partition_method = "kmeans",
    concordance_cutoff = 0.9, PAC_cutoff = 0.2, silhouette_cutoff = 0.5,
    min_samples = 6, min_signatures = 50, max_k = 4, verbose = TRUE,
    mc.cores = 1, ...)

Arguments

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.

concordance_cutoff

the cutoff of concordance scores to determine whether to continue looking for subgroups. Currently it is not used.

PAC_cutoff

the cutoff of PAC scores to determine whether to continue looking for subgroups.

silhouette_cutoff

cutoff for silhouette scores.

min_samples

the cutoff of number of samples to determine whether to continue looking for subgroups.

min_signatures

minimal number of signatures to determine whether to continue looking for subgroups.

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

Details

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.

Value

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

Author(s)

Zuguang Gu <z.gu@dkfz.de>

Examples

## Not run: 
set.seed(123)
m = cbind(rbind(matrix(rnorm(20*20, mean = 2, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20)),
          rbind(matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 1, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20)),
          rbind(matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 1, sd = 0.3), nr = 20))
         ) + matrix(rnorm(60*60, sd = 0.5), nr = 60)
cola_rh = hierarchical_partition(m, top_n = c(20, 30, 40), PAC_cutoff = 0.3)

## End(Not run)
data(cola_rh)
cola_rh

[Package cola version 1.0.0 Index]