consensus_partition_by_down_sampling {cola} | R Documentation

## Consensus partitioning only with a subset of columns

### Description

Consensus partitioning only with a subset of columns

### Usage

    consensus_partition_by_down_sampling(data,
        top_value_method = "ATC",
        top_n = NULL,
        partition_method = "skmeans",
        max_k = 6, k = NULL,
        subset = min(round(ncol(data)*0.2), 250), pre_select = TRUE,
        verbose = TRUE, prefix = "", anno = NULL, anno_col = NULL,
        dist_method = c("euclidean", "correlation", "cosine"),
        .env = NULL, .predict = TRUE, mc.cores = 1, cores = mc.cores, ...)

### 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.
`subset` | Number of columns to randomly sample, or a vector of selected indices.
`pre_select` | Whether to pre-select by k-means.
`verbose` | Whether to print messages.
`prefix` | Internally used.
`anno` | Annotation data frame.
`anno_col` | Annotation colors.
`dist_method` | Method for predict the class for other columns.
`.env` | An environment, internally used.
`.predict` | Internally used.
`mc.cores` | Number of cores. This argument will be removed in future versions.
`cores` | Number of cores, or a `cluster` object returned by `makeCluster`.
`...` | All pass to `consensus_partition`.

### Details

The function performs consensus partitioning only with a small subset of columns and the class of other columns are predicted by `predict_classes,ConsensusPartition-method`.

### Examples

    ## Not run:
    data(golub_cola)
    m = get_matrix(golub_cola)

    set.seed(123)
    golub_cola_ds = consensus_partition_by_down_sampling(m, subset = 50,
    	anno = get_anno(golub_cola), anno_col = get_anno_col(golub_cola),
    	top_value_method = "SD", partition_method = "kmeans")

    ## End(Not run)

[Package cola version 1.8.1 Index]