dimension_reduction-DownSamplingConsensusPartition-method {cola} | R Documentation

## Visualize column after dimension reduction

### Description

Visualize samples (the matrix columns) after dimension reduction

### Usage

    ## S4 method for signature 'DownSamplingConsensusPartition'
    dimension_reduction(object, k, top_n = NULL,
        method = c("PCA", "MDS", "t-SNE", "UMAP"),
        control = list(),
        internal = FALSE, nr = 5000,
        p_cutoff = 0.05, remove = FALSE,
        scale_rows = TRUE, verbose = TRUE, ...)

### Arguments

`object` | A `DownSamplingConsensusPartition-class` object.
`k` | Number of subgroups.
`top_n` | Top n rows to use. By default it uses all rows in the original matrix.
`method` | Which method to reduce the dimension of the data. `MDS` uses `cmdscale`, `PCA` uses `prcomp`. `t-SNE` uses `Rtsne`. `UMAP` uses `umap`.
`control` | A list of parameters for `Rtsne` or `umap`.
`internal` | Internally used.
`nr` | If number of matrix rows is larger than this value, random `nr` rows are used.
`p_cutoff` | Cutoff of p-value of class label prediction. Data points with values higher than it will be mapped with cross symbols.
`remove` | Whether to remove columns which have high p-values than the cutoff.
`scale_rows` | Whether to perform scaling on matrix rows.
`verbose` | Whether print messages.
`...` | Other arguments.

### Details

This function is basically very similar as `dimension_reduction,ConsensusPartition-method`.

### Value

No value is returned.

### Examples

    data(golub_cola_ds)
    dimension_reduction(golub_cola_ds, k = 2)
    dimension_reduction(golub_cola_ds, k = 3)

[Package cola version 1.6.0 Index]