plot_cosine_heatmap {MutationalPatterns} | R Documentation

## Plot cosine similarity heatmap

### Description

Plot pairwise cosine similarities in a heatmap.

### Usage

    plot_cosine_heatmap(cos_sim_matrix, col_order, cluster_rows = TRUE,
      method = "complete", plot_values = FALSE)

### Arguments

`cos_sim_matrix` | Matrix with pairwise cosine similarities. Result from `cos_sim_matrix`
`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.

### Value

Heatmap with cosine similarities

### See Also

`mut_matrix`, `cos_sim_matrix`, `cluster_signatures`

### Examples

    ## 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")

[Package MutationalPatterns version 2.0.0 Index]