run_all_consensus_partition_methods {cola}R Documentation

Consensus partition for all combinations of methods

Description

Consensus partition for all combinations of methods

Usage

run_all_consensus_partition_methods(data,
    top_value_method = all_top_value_methods(),
    partition_method = all_partition_methods(),
    max_k = 6,
    top_n = seq(min(1000, round(nrow(data)*0.1)),
    min(5000, round(nrow(data)*0.5)),
    length.out = 5),
    mc.cores = 1, anno = NULL, anno_col = NULL,
    sample_by = "row", p_sampling = 0.8, partition_repeat = 50,
    scale_rows = NULL, verbose = TRUE)

Arguments

data

A numeric matrix where subgroups are found by columns.

top_value_method

Method which are used to extract top n rows. Allowed methods are in all_top_value_methods and can be self-added by register_top_value_methods.

partition_method

Method which are used to do partition on samples. Allowed methods are in all_partition_methods and can be self-added by register_partition_methods.

max_k

Maximal number of partitions to try. The function will try 2:max_k partitions.

top_n

Number of rows with top values. The value can be a vector with length > 1. When n > 5000, the function only randomly sample 5000 rows from top n rows. If top_n is a vector, paritition will be applied to every values in top_n and consensus partition is summarized from all partitions.

mc.cores

Number of cores to use.

anno

A data frame with known annotation of columns.

anno_col

A list of colors (color is defined as a named vector) for the annotations. If anno is a data frame, anno_col should be a named list where names correspond to the column names in anno.

sample_by

Should randomly sample the matrix by rows or by columns?

p_sampling

Proportion of the top n rows to sample.

partition_repeat

Number of repeats for the random sampling.

scale_rows

Whether to scale rows. If it is TRUE, scaling method defined in register_partition_methods is used.

verbose

Ahether to print messages.

Details

The function runs consensus partitioning by consensus_partition for all combinations of top-value methods and partition methods.

It also adjsuts the class IDs for all methods and for all k to make them as consistent as possible.

Value

A ConsensusPartitionList-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 = 1), nr = 20),
                matrix(rnorm(20*20, mean = -1), nr = 20)),
          rbind(matrix(rnorm(20*20, mean = -1), nr = 20),
                matrix(rnorm(20*20, mean = 1), nr = 20))
         ) + matrix(rnorm(40*40), nr = 40)
rl = run_all_consensus_partition_methods(data = m, top_n = c(20, 30, 40))

## End(Not run)
data(cola_rl)
cola_rl

[Package cola version 1.0.1 Index]