View source: R/plot_original_vs_reconstructed.R
plot_original_vs_reconstructed | R Documentation |
When a reconstructed profile has a cosine similarity of more than 0.95 with original, the reconstructed profile is considered very good.
plot_original_vs_reconstructed( mut_matrix, reconstructed, y_intercept = 0.95, ylims = c(0.6, 1) )
mut_matrix |
mutation count matrix (dimensions: x mutation types X n samples) |
reconstructed |
A reconstructed mutation count matrix |
y_intercept |
The y intercept of the plotted horizontal line. Default: 0.95. |
ylims |
The limits of the y axis. Default: c(0.6, 1) |
A ggplot figure
## See the 'mut_matrix()' example for how we obtained the 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" )) ## Create figure plot_original_vs_reconstructed(mut_mat, nmf_res$reconstructed) ## You can also use the results of signature refitting. ## Here we load some data as an example fit_res <- readRDS(system.file("states/snv_refit.rds", package = "MutationalPatterns" )) plot_original_vs_reconstructed(mut_mat, fit_res$reconstructed) ## You can also change the height of the horizontal line plot_original_vs_reconstructed(mut_mat, fit_res$reconstructed, y_intercept = 0.90) ## It's also possible to change the limits of the y axis plot_original_vs_reconstructed(mut_mat, fit_res$reconstructed, ylims = c(0, 1))
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