Visualize column after dimension reduction — dimension_reduction-DownSamplingConsensusPartition-method • cola

Visualize samples (the matrix columns) after dimension reduction

# S4 method for DownSamplingConsensusPartition
dimension_reduction(object, k, top_n = NULL,
    method = c("PCA", "MDS", "t-SNE", "UMAP"),
    control = list(), color_by = NULL,
    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.
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.
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)
#> use UMAP

dimension_reduction(golub_cola_ds, k = 3)
#> use UMAP