run_all_consensus_partition_methods: Consensus partitioning for all combinations of methods
In cola: A Framework for Consensus Partitioning

Description Usage Arguments Details Value Author(s) Examples

Consensus partitioning for all combinations of methods

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(3000, round(nrow(data)*0.3)),
    length.out = 3),
    mc.cores = 1, anno = NULL, anno_col = NULL,
    sample_by = "row", p_sampling = 0.8, partition_repeat = 50,
    scale_rows = NULL, verbose = TRUE)

`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 partition samples. Allowed methods are in `all_partition_methods` and can be self-added by `register_partition_methods`.
`max_k`	Maximal number of subgroups to try. The function will try `2:max_k` subgroups.
`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`	Whether to print messages.

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

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

A ConsensusPartitionList-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>

## 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)