dimension_reduction-ConsensusPartition-method: Visualize column after dimension reduction
In cola: A Framework for Consensus Partitioning

Description Usage Arguments Value Author(s) Examples

Visualize samples (the matrix columns) after dimension reduction

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

`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`.
`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.