dimension_reduction-ConsensusPartition-method {cola} | R Documentation

## Visualize column after dimension reduction

### Description

Visualize samples (the matrix columns) after dimension reduction

### Usage

    ## S4 method for signature 'ConsensusPartition'
    dimension_reduction(object, k, top_n = NULL,
        method = c("PCA", "MDS", "t-SNE", "UMAP"),
        control = list(), color_by = NULL,
        internal = FALSE, nr = 5000,
        silhouette_cutoff = 0.5, remove = FALSE,
        scale_rows = object@scale_rows, verbose = TRUE, ...)

### Arguments

`object` | A `ConsensusPartition-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`.
`color_by` | If annotation table is set, an annotation name can be set here.
`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.
`silhouette_cutoff` | Cutoff of silhouette score. Data points with values less than it will be mapped with cross symbols.
`remove` | Whether to remove columns which have less silhouette scores than the cutoff.
`scale_rows` | Whether to perform scaling on matrix rows.
`verbose` | Whether print messages.
`...` | Other arguments.

### Value

Locations of the points.

### Author(s)

Zuguang Gu <z.gu@dkfz.de>

### Examples

    data(golub_cola)
    dimension_reduction(golub_cola["ATC", "skmeans"], k = 3)

[Package cola version 1.8.1 Index]