Description Usage Arguments Details Value Author(s) Examples
View source: R/EnrichedHeatmap.R
Constructor Method for the Enriched Heatmap
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | EnrichedHeatmap(mat,
col,
top_annotation = HeatmapAnnotation(enriched = anno_enriched()),
row_order = order(enriched_score(mat), decreasing = TRUE),
pos_line = TRUE,
pos_line_gp = gpar(lty = 2),
axis_name = NULL,
axis_name_rot = 0,
axis_name_gp = gpar(fontsize = 10),
border = TRUE,
cluster_rows = FALSE,
row_dend_reorder = -enriched_score(mat),
show_row_dend = FALSE,
show_row_names = FALSE,
heatmap_legend_param = list(),
...)
|
mat |
A matrix which is returned by |
col |
Color settings. If the signals are categorical, color should be a vector with category levels as names. |
top_annotation |
A special annotation which is always put on top of the enriched heatmap and is constructed by |
row_order |
Row order. Default rows are ordered by enriched scores calculated from |
pos_line |
Whether draw vertical lines which represent the positions of |
pos_line_gp |
Graphic parameters for the position lines. |
axis_name |
Names for axis which is below the heatmap. If the targets are single points, |
axis_name_rot |
Rotation for axis names. |
axis_name_gp |
Graphic parameters for axis names. |
border |
Whether show the border of the heatmap? |
cluster_rows |
Clustering on rows are turned off by default. |
show_row_dend |
Whether show dendrograms on rows if hierarchical clustering is applied on rows? |
row_dend_reorder |
Weight for reordering the row dendrogram. It is reordered by enriched scores by default. |
show_row_names |
Whether show row names? |
heatmap_legend_param |
A list of settings for heatmap legends. |
... |
Other arguments passed to |
The enriched heatmap is essentially a normal heatmap but with several special settings. Following parameters are set with pre-defined values:
cluster_columns
enforced to be FALSE
show_column_names
enforced to be FALSE
bottom_annotation
enforced to be NULL
EnrichedHeatmap
calls Heatmap
, thus, most of the
arguments in Heatmap
are usable in EnrichedHeatmap
such as
to apply clustering on rows, or to split rows by a data frame or k-means clustering. Users can also
add more than one heatmaps by +
operator. Enriched heatmaps and normal heatmaps can be
concatenated mixed.
For detailed demonstration, please go to the vignette.
A Heatmap-class
object.
Zuguang Gu <z.gu@dkfz.de>
1 2 3 4 5 6 | load(system.file("extdata", "chr21_test_data.RData", package = "EnrichedHeatmap"))
mat3 = normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute",
extend = 5000, w = 50, smooth = TRUE)
EnrichedHeatmap(mat3, name = "methylation", column_title = "methylation near CGI")
EnrichedHeatmap(mat3, name = "meth1") + EnrichedHeatmap(mat3, name = "meth2")
# for more examples, please go to the vignette
|
Loading required package: grid
Loading required package: ComplexHeatmap
========================================
ComplexHeatmap version 2.6.2
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
genomic data. Bioinformatics 2016.
This message can be suppressed by:
suppressPackageStartupMessages(library(ComplexHeatmap))
========================================
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Loading required package: S4Vectors
Attaching package: ‘S4Vectors’
The following object is masked from ‘package:base’:
expand.grid
Loading required package: IRanges
Loading required package: GenomeInfoDb
========================================
EnrichedHeatmap version 1.20.0
Bioconductor page: http://bioconductor.org/packages/EnrichedHeatmap/
Github page: https://github.com/jokergoo/EnrichedHeatmap
Documentation: http://bioconductor.org/packages/EnrichedHeatmap/
If you use it in published research, please cite:
Gu, Z. EnrichedHeatmap: an R/Bioconductor package for comprehensive
visualization of genomic signal associations. BMC Genomics 2018.
This message can be suppressed by:
suppressPackageStartupMessages(library(EnrichedHeatmap))
========================================
The automatically generated colors map from the 1^st and 99^th of the
values in the matrix. There are outliers in the matrix whose patterns
might be hidden by this color mapping. You can manually set the color
to `col` argument.
Use `suppressMessages()` to turn off this message.
The automatically generated colors map from the 1^st and 99^th of the
values in the matrix. There are outliers in the matrix whose patterns
might be hidden by this color mapping. You can manually set the color
to `col` argument.
Use `suppressMessages()` to turn off this message.
The automatically generated colors map from the 1^st and 99^th of the
values in the matrix. There are outliers in the matrix whose patterns
might be hidden by this color mapping. You can manually set the color
to `col` argument.
Use `suppressMessages()` to turn off this message.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.