consensus_heatmap-ConsensusPartition-method | R Documentation |
Heatmap of the consensus matrix
## S4 method for signature '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, ...)
object |
A |
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 |
anno_col |
A list of colors (color is defined as a named vector) for the annotations. If |
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. |
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.
No value is returned.
Zuguang Gu <z.gu@dkfz.de>
membership_heatmap,ConsensusPartition-method
data(golub_cola)
consensus_heatmap(golub_cola["ATC", "skmeans"], k = 3)
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