dimension_reduction-DownSamplingConsensusPartition-method: Visualize column after dimension reduction

In cola: A Framework for Consensus Partitioning

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)

cola documentation built on Nov. 8, 2020, 8:12 p.m.