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 = NA,
      row_order = NA,
      cluster_rows = TRUE,
      cluster_cols = FALSE,
      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.
`row_order` | Character vector with the desired order of the row names for plotting. Optional.
`cluster_rows` | Hierarchically cluster rows based on eucledian distance. Default = TRUE.
`cluster_cols` | Hierarchically cluster cols based on eucledian distance. Default = FALSE.
`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`

### 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"
    ))

    ## Get signatures
    signatures <- get_known_signatures()

    ## Calculate the cosine similarity between each signature and each 96 mutational profile
    cos_matrix <- cos_sim_matrix(mut_mat, signatures)

    ## Plot the cosine similarity between each signature and each sample with hierarchical
    ## clustering of samples and signatures.
    plot_cosine_heatmap(cos_matrix, cluster_rows = TRUE, cluster_cols = TRUE)

    ## In the above example, clustering is performed on the similarities of the samples with
    ## the signatures. It's also possible to cluster the signatures and samples on their (96) profile.
    ## This will generally give better results
    ## If you use the same signatures for different analyses,
    ## then their order will also be consistent.
    hclust_cosmic <- cluster_signatures(signatures, method = "average")
    cosmic_order <- colnames(signatures)[hclust_cosmic$order]
    hclust_samples <- cluster_signatures(mut_mat, method = "average")
    sample_order <- colnames(mut_mat)[hclust_samples$order]
    ## Plot the cosine heatmap using this given signature order.
    plot_cosine_heatmap(cos_matrix,
      cluster_rows = FALSE, cluster_cols = FALSE,
      row_order = sample_order, col_order = cosmic_order
    )

    ## You can also plot the similarity of samples with eachother
    cos_matrix <- cos_sim_matrix(mut_mat, mut_mat)
    plot_cosine_heatmap(cos_matrix, cluster_rows = TRUE, cluster_cols = TRUE)

    ## It's also possible to add the actual values in the heatmap.
    plot_cosine_heatmap(cos_matrix, cluster_rows = TRUE, cluster_cols = TRUE, plot_values = TRUE)

[Package MutationalPatterns version 3.0.1 Index]