Description Usage Arguments Details Value Examples
Given a matrix expressing the crosssimilarity between two (possibly different) sets of entities, this will reorder it to move the high values close to the diagonal, for a better visualization.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  sheatmap(
data,
...,
order_data = NULL,
annotation_col = NULL,
annotation_row = NULL,
order_rows = TRUE,
order_cols = TRUE,
squared_order = TRUE,
same_order = FALSE,
patch_cols_order = NULL,
patch_rows_order = NULL,
discount_outliers = TRUE,
cluster_rows = TRUE,
cluster_cols = TRUE,
oclust_rows = TRUE,
oclust_cols = TRUE,
clustering_distance_rows = "euclidian",
clustering_distance_cols = "euclidian",
clustering_method = "ward.D2",
clustering_callback = NA
)

data 
A rectangular matrix to plot, of nonnegative values (unless 
... 
Additional flags to pass to 
order_data 
An optional matrix of nonnegative values of the same size to use for computing the orders. 
annotation_col 
Optional data frame describing each column. 
annotation_row 
Optional data frame describing each row. 
order_rows 
Whether to reorder the rows. Otherwise, use the current order. 
order_cols 
Whether to reorder the columns. Otherwise, use the current order. 
squared_order 
Whether to reorder to minimize the l2 norm (otherwise minimizes the l1 norm). 
same_order 
Whether to apply the same order to both rows and columns (if reordering both). For a square matrix, may also contain 'row' or 'column' to force the order of one axis to apply to both. 
patch_cols_order 
Optional function that may be applied to the columns order, returning a better order. 
patch_rows_order 
Optional function that may be applied to the rows order, returning a better order. 
discount_outliers 
Whether to do a final order phase discounting outlier values far from the diagonal. 
cluster_rows 
Whether to cluster the rows, or the clustering to use. 
cluster_cols 
Whether to cluster the columns, or the clustering to use. 
oclust_rows 
Whether to use 
oclust_cols 
Whether to use 
clustering_distance_rows 
The default method for computing row distances (by default,

clustering_distance_cols 
The default method for computing column distances (by default,

clustering_method 
The default method to use for hierarchical clustering (by default,

clustering_callback 
Is not supported. 
If you have an apriori order for the rows and/or columns, you can prevent reordering either or
both by specifying order_rows=FALSE
and/or order_cols=FALSE
. Otherwise,
slanted_orders
is used to compute the "ideal" slanted order for the data.
By default, the rows and columns are ordered independently from each other. If the matrix is
asymmetric but square (e.g., a matrix of weights of a directed graph such as a
Knearestneighbors graph), then you can can specify same_order=TRUE
to force both rows
and columns to the same order. You can also specify same_order='row'
to force the columns
to use the same order as the rows, or same_order='column'
to force the rows to use the
same order as the columns.
You can also specify a patch_cols_order
and/or a 'patch_rows_order
' function that
takes the computed "ideal" order and returns a patched order. For example, this can be used to
force special values (such as "outliers") to the side of the heatmap.
There are four options for controlling clustering:
* By default, sheatmap
will generate a clustering tree using oclust
, to generate
the "best" clustering that is also compatible with the slanted order.
* Request that sheatmap
will use the same hclust
as
pheatmap
(e.g., oclust_rows=FALSE
). In this case, the tree is reordered to
be the "most compatible" with the target slanted order. That is, sheatmap
will invoke
reorder_hclust
so that, for each node of the tree, the order of the two subtrees will
be chosen to best match the target slanted order. The end result need not be identical to the
slanted order, but is as close as possible given the hclust
clustering tree.
* Specify an explicit clustering (e.g., cluster_rows=hclust(...)
). In this case,
sheatmap
will again merely reorder the tree but will not modify it.
In addition, you can give this function any of the pheatmap
flags, and it will just pass
them on. This allows full control over the diagram's features.
Note that clustering_callback
is not supported. In addition, the default
clustering_method
here is ward.D2
instead of complete
, since the only
methods supported by oclust
are ward.D
and ward.D2
.
Whatever pheatmap
returns.
1 2 3 
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