consensus_partition: Consensus partition

View source: R/consensus_partition.R

consensus_partitionR Documentation

Consensus partition

Description

Consensus partition

Usage

consensus_partition(data,
    top_value_method = "ATC",
    top_n = NULL,
    partition_method = "skmeans",
    max_k = 6,
    k = NULL,
    sample_by = "row",
    p_sampling = 0.8,
    partition_repeat = 50,
    partition_param = list(),
    anno = NULL,
    anno_col = NULL,
    scale_rows = NULL,
    verbose = TRUE,
    mc.cores = 1, cores = mc.cores,
    prefix = "",
    .env = NULL,
    help = cola_opt$help)

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. Use register_top_value_methods to add a new top-value method.

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.

partition_method

A single partitioning method. Available methods are in all_partition_methods. Use register_partition_methods to add a new partition method.

max_k

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

k

Alternatively, you can specify a vector k.

sample_by

Should randomly sample the matrix by rows or by columns?

p_sampling

Proportion of the submatrix which contains the top n rows to sample.

partition_repeat

Number of repeats for the random sampling.

partition_param

Parameters for the partition method which are passed to ... in a registered partitioning method. See register_partition_methods for detail.

anno

A data frame with known annotation of samples. The annotations will be plotted in heatmaps and the correlation to predicted subgroups will be tested.

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.

scale_rows

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

verbose

Whether print messages.

mc.cores

Multiple cores to use. This argument will be removed in future versions.

cores

Number of cores, or a cluster object returned by makeCluster.

prefix

Internally used.

.env

An environment, internally used.

help

Whether to print help messages.

Details

The function performs analysis in following steps:

  • calculate scores for rows by top-value method,

  • for each top_n value, take top n rows,

  • randomly sample p_sampling rows from the top_n-row matrix and perform partitioning for partition_repeats times,

  • collect partitions from all individual partitions and summarize a consensus partition.

Value

A ConsensusPartition-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>

See Also

run_all_consensus_partition_methods runs consensus partitioning with multiple top-value methods and multiple partitioning methods.

Examples

set.seed(123)
m = cbind(rbind(matrix(rnorm(20*20, mean = 1,   sd = 0.5), nr = 20),
                matrix(rnorm(20*20, mean = 0,   sd = 0.5), nr = 20),
                matrix(rnorm(20*20, mean = 0,   sd = 0.5), nr = 20)),
          rbind(matrix(rnorm(20*20, mean = 0,   sd = 0.5), nr = 20),
                matrix(rnorm(20*20, mean = 1,   sd = 0.5), nr = 20),
                matrix(rnorm(20*20, mean = 0,   sd = 0.5), nr = 20)),
          rbind(matrix(rnorm(20*20, mean = 0.5, sd = 0.5), nr = 20),
                matrix(rnorm(20*20, mean = 0.5, sd = 0.5), nr = 20),
                matrix(rnorm(20*20, mean = 1,   sd = 0.5), nr = 20))
         ) + matrix(rnorm(60*60, sd = 0.5), nr = 60)
res = consensus_partition(m, partition_repeat = 10, top_n = c(10, 20, 50))
res

jokergoo/cola documentation built on Feb. 29, 2024, 1:41 a.m.