consensus_partition: Consensus partition
In cola: A Framework for Consensus Partitioning

Description Usage Arguments Details Value Author(s) See Also Examples

Consensus partition

consensus_partition(data,
    top_value_method = "ATC",
    top_n = seq(min(1000, round(nrow(data)*0.1)),
    min(3000, round(nrow(data)*0.3)),
    length.out = 3),
    partition_method = "skmeans",
    max_k = 6,
    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,
    prefix = "",
    .env = NULL)

`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
`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.
`prefix`	Internally used.
`.env`	An environment, internally used.

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.

A ConsensusPartition-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>

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

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