run_all_consensus_partition_methods {cola} | R Documentation

## Consensus partitioning for all combinations of methods

### Description

Consensus partitioning 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 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` | Ahether to print messages.

### Details

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.

### 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 1.4.1 Index]