plot_cosine_heatmap {MutationalPatterns} | R Documentation |
Plot pairwise cosine similarities in a heatmap.
plot_cosine_heatmap(cos_sim_matrix, col_order, cluster_rows = TRUE, method = "complete", plot_values = FALSE)
cos_sim_matrix |
Matrix with pairwise cosine similarities.
Result from |
col_order |
Character vector with the desired order of the columns names for plotting. Optional. |
cluster_rows |
Hierarchically cluster rows based on eucledian distance. Default = TRUE. |
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 cosine similarity values in heatmap. Default = FALSE. |
Heatmap with cosine similarities
mut_matrix
,
cos_sim_matrix
,
cluster_signatures
## See the 'mut_matrix()' example for how we obtained the mutation matrix: mut_mat <- readRDS(system.file("states/mut_mat_data.rds", package="MutationalPatterns")) ## You can download the signatures from the COSMIC: # http://cancer.sanger.ac.uk/cancergenome/assets/signatures_probabilities.txt ## We copied the file into our package for your convenience. filename <- system.file("extdata/signatures_probabilities.txt", package="MutationalPatterns") cancer_signatures <- read.table(filename, sep = "\t", header = TRUE) ## Match the order to MutationalPatterns standard of mutation matrix order = match(row.names(mut_mat), cancer_signatures$Somatic.Mutation.Type) ## Reorder cancer signatures dataframe cancer_signatures = cancer_signatures[order,] ## Use trinucletiode changes names as row.names ## row.names(cancer_signatures) = cancer_signatures$Somatic.Mutation.Type ## Keep only 96 contributions of the signatures in matrix cancer_signatures = as.matrix(cancer_signatures[,4:33]) ## Rename signatures to number only colnames(cancer_signatures) = as.character(1:30) ## Calculate the cosine similarity between each signature and each 96 mutational profile cos_matrix = cos_sim_matrix(mut_mat, cancer_signatures) ## Cluster signatures based on cosine similarity sig_hclust = cluster_signatures(cancer_signatures) col_order = colnames(cancer_signatures)[sig_hclust$order] ## Plot the cosine similarity between each signature and each sample with hierarchical ## sample clustering and signatures order based on similarity plot_cosine_heatmap(cos_matrix, col_order, cluster_rows = TRUE, method = "complete")