plot_contribution_heatmap {MutationalPatterns} | R Documentation

## Plot signature contribution heatmap

### Description

Plot relative contribution of signatures in a heatmap

### Usage

    plot_contribution_heatmap(
      contribution,
      sig_order = NA,
      sample_order = NA,
      cluster_samples = TRUE,
      cluster_sigs = FALSE,
      method = "complete",
      plot_values = FALSE
    )

### Arguments

`contribution` | Signature contribution matrix
`sig_order` | Character vector with the desired order of the signature names for plotting. Optional.
`sample_order` | Character vector with the desired order of the sample names for plotting. Optional.
`cluster_samples` | Hierarchically cluster samples based on eucledian distance. Default = T.
`cluster_sigs` | Hierarchically cluster sigs 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.

### Value

Heatmap with relative contribution of each signature for each sample

### See Also

`extract_signatures`, `mut_matrix`, `plot_contribution`, `plot_cosine_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)

[Package MutationalPatterns version 3.0.1 Index]