1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_contribution_heatmap.R
\name{plot_contribution_heatmap}
\alias{plot_contribution_heatmap}
\title{Plot signature contribution heatmap}
\usage{
plot_contribution_heatmap(
contribution,
sig_order = NA,
sample_order = NA,
cluster_samples = TRUE,
cluster_sigs = FALSE,
method = "complete",
plot_values = FALSE
)
}
\arguments{
\item{contribution}{Signature contribution matrix}
\item{sig_order}{Character vector with the desired order of the signature names for plotting. Optional.}
\item{sample_order}{Character vector with the desired order of the sample names for plotting. Optional.}
\item{cluster_samples}{Hierarchically cluster samples based on euclidean distance. Default = T.}
\item{cluster_sigs}{Hierarchically cluster sigs based on euclidean distance. Default = T.}
\item{method}{The agglomeration method to be used for hierarchical clustering. This should be one of
"ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC)
or "centroid" (= UPGMC). Default = "complete".}
\item{plot_values}{Plot relative contribution values in heatmap. Default = F.}
}
\value{
Heatmap with relative contribution of each signature for each sample
}
\description{
Plot relative contribution of signatures in a heatmap
}
\examples{
## Extracting signatures can be computationally intensive, so
## we use pre-computed data generated with the following command:
# nmf_res <- extract_signatures(mut_mat, rank = 2)
nmf_res <- readRDS(system.file("states/nmf_res_data.rds",
package = "MutationalPatterns"
))
## Set signature names as row names in the contribution matrix
rownames(nmf_res$contribution) <- c("Signature A", "Signature B")
## Plot with clustering.
plot_contribution_heatmap(nmf_res$contribution, cluster_samples = TRUE, cluster_sigs = TRUE)
## Define signature and sample order for plotting. If you have a mutation or signature
## matrix, then this can be done like in the example of 'plot_cosine_heatmap()'
sig_order <- c("Signature B", "Signature A")
sample_order <- c(
"colon1", "colon2", "colon3", "intestine1", "intestine2",
"intestine3", "liver3", "liver2", "liver1"
)
plot_contribution_heatmap(nmf_res$contribution,
cluster_samples = FALSE,
sig_order = sig_order, sample_order = sample_order
)
## It's also possible to create a contribution heatmap with text values
output_text <- plot_contribution_heatmap(nmf_res$contribution, plot_values = TRUE)
## This function can also be used on the result of a signature refitting analysis.
## Here we load a existing result as an example.
snv_refit <- readRDS(system.file("states/strict_snv_refit.rds",
package = "MutationalPatterns"
))
plot_contribution_heatmap(snv_refit$contribution, cluster_samples = TRUE, cluster_sigs = TRUE)
}
\seealso{
\code{\link{extract_signatures}},
\code{\link{mut_matrix}},
\code{\link{plot_contribution}},
\code{\link{plot_cosine_heatmap}}
}
|
