Heatmap of the consensus matrix

Heatmap of the consensus matrix

# S4 method for ConsensusPartition
consensus_heatmap(object, k, internal = FALSE,
    anno = object@anno, anno_col = get_anno_col(object),
    show_row_names = FALSE, show_column_names = FALSE, row_names_gp = gpar(fontsize = 8),
    simplify = FALSE, ...)

Arguments

object

A ConsensusPartition-class object.

k

Number of subgroups.

internal

Used internally.

anno

A data frame of annotations for the original matrix columns. By default it uses the annotations specified in consensus_partition or run_all_consensus_partition_methods.

anno_col

A list of colors (color is defined as a named vector) for the annotations. If anno is a data frame, anno_col should be a named list where names correspond to the column names in anno.

show_row_names

Whether plot row names on the consensus heatmap (which are the column names in the original matrix)

show_column_names

Whether show column names.

row_names_gp

Graphics parameters for row names.

simplify

Internally used.

...

other arguments.

Details

For row i and column j in the consensus matrix, the value of corresponding x_ij is the probability of sample i and sample j being in a same group from all partitions.

There are following heatmaps from left to right:

  • probability of the sample to stay in the corresponding group

  • silhouette scores which measure the distance of an item to the second closest subgroups.

  • predicted subgroups

  • consensus matrix.

  • more annotations if provided as anno

One thing that is very important to note is that since we already know the consensus subgroups from consensus partition, in the heatmap, only rows or columns within the group is clustered.

Value

No value is returned.

Author

Zuguang Gu <z.gu@dkfz.de>

Examples

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