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, subset = min(round(ncol(data)*0.2), 250),
        verbose = TRUE, prefix = "", anno = NULL, anno_col = NULL,
        dist_method = c("euclidean", "correlation", "cosine"),
        .env = NULL, ...)

### Arguments

`data` | A numeric matrix where subgroups are found by columns.
`subset` | Number of columns to randomly sample, or a vector of selected indices.
`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.
`...` | 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 1.6.0 Index]