plot_contribution_heatmap {MutationalPatterns} | R Documentation |
Plot relative contribution of signatures in a heatmap
plot_contribution_heatmap(contribution, sig_order, cluster_samples = TRUE, method = "complete", plot_values = FALSE)
contribution |
Signature contribution matrix |
sig_order |
Character vector with the desired order of the signature names for plotting. Optional. |
cluster_samples |
Hierarchically cluster samples based on eucledian distance. Default = T. |
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". |
plot_values |
Plot relative contribution values in heatmap. Default = F. |
Heatmap with relative contribution of each signature for each sample
extract_signatures
,
mut_matrix
,
plot_contribution
## See the 'mut_matrix()' example for how we obtained the following ## mutation matrix. mut_mat <- readRDS(system.file("states/mut_mat_data.rds", package="MutationalPatterns")) ## 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$contribution) = c("Signature A", "Signature B") ## Define signature order for plotting sig_order = c("Signature B", "Signature A") ## Contribution heatmap with signatures in defined order plot_contribution_heatmap(nmf_res$contribution, sig_order = c("Signature B", "Signature A")) ## Contribution heatmap without sample clustering plot_contribution_heatmap(nmf_res$contribution, sig_order = c("Signature B", "Signature A"), cluster_samples = FALSE, method = "complete")