comparison mutational_patterns.R @ 25:b00fef2b1c2c draft

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/mutational_patterns commit eeb46055822c6805c209af0c450ae941100960bd

24:ca6c19ee7da0

    make_option(
    "--newsignum",
    default = 2,
    type = "integer",
    help = "Number of new signatures to be captured"
  ),
  make_option(
    "--output_spectrum",
    default = NA,
    type = "character",

    # (For larger datasets it is wise to perform more iterations by changing the nrun parameter
    # to achieve stability and avoid local minima)
    nmf_res <- extract_signatures(pseudo_mut_mat, rank = opt$newsignum, nrun = opt$nrun)
    # Assign signature COSMICv3.2 names
    cosmic_signatures <- get_known_signatures()
    nmf_res <- rename_nmf_signatures(nmf_res, cosmic_signatures, cutoff = 0.85)
    sim_matrix <- cos_sim_matrix(cosmic_signatures, nmf_res$signatures)
    plot_cosine_sim <- plot_cosine_heatmap(sim_matrix)
    grid.arrange(plot_cosine_sim)
    # Plot the 96-profile of the signatures:
    p5 <- plot_96_profile(nmf_res$signatures, condensed = TRUE)
    new_sig_matrix <- reshape2::dcast(p5$data, substitution + context ~ sample, value.var = "freq")
    new_sig_matrix <- format(new_sig_matrix, scientific = TRUE)
    newcol <- paste0(gsub("\\..", "", new_sig_matrix$context, perl = TRUE),
                     "[", new_sig_matrix$substitution, "]",
                     gsub("^.\\.", "", new_sig_matrix$context, perl = TRUE))
    new_sig_matrix <- cbind(Type = newcol, new_sig_matrix[, seq_along(new_sig_matrix)[-c(1, 2)]])
    write.table(new_sig_matrix, file = opt$sigmatrix, quote = FALSE, row.names = FALSE, sep = "\t")
    grid.arrange(p5)
    # Visualize the contribution of the signatures in a barplot
    pc1 <- plot_contribution(nmf_res$contribution, nmf_res$signature, mode = "relative", coord_flip = TRUE)
    # Visualize the contribution of the signatures in absolute number of mutations
    pc2 <- plot_contribution(nmf_res$contribution, nmf_res$signature, mode = "absolute", coord_flip = TRUE)
    # Combine the two plots:

    # The relative contribution of each signature for each sample can also be plotted as a heatmap with
    # plot_contribution_heatmap, which might be easier to interpret and compare than stacked barplots.
    # The samples can be hierarchically clustered based on their euclidean dis- tance. The signatures
    # can be plotted in a user-specified order.
    # Plot signature contribution as a heatmap with sample clustering dendrogram and a specified signature order:
    pch1 <- plot_contribution_heatmap(nmf_res$contribution,
                                      sig_order = paste0("NewSig_", 1:opt$newsignum))
    # Plot signature contribution as a heatmap without sample clustering:
    pch2 <- plot_contribution_heatmap(nmf_res$contribution, cluster_samples = FALSE)
    #Combine the plots into one figure:
    grid.arrange(pch1, pch2, ncol = 2, widths = c(2, 1.6))

    # Compare the reconstructed mutational profile with the original mutational profile:
    plot_compare_profiles(pseudo_mut_mat[, 1],
                          nmf_res$reconstructed[, 1],
                          profile_names = c("Original", "Reconstructed"),
                          condensed = TRUE)
    dev.off()
    }

##### Section 3: Find optimal contribution of known signatures: COSMIC or OWN mutational signatures ####

25:b00fef2b1c2c

    make_option(
    "--newsignum",
    default = 2,
    type = "integer",
    help = "Number of new signatures to be captured"
  ),
    make_option(
    "--cosmic_id_threshold",
    default = 0.85,
    type = "double",
    help = "minimu cosine similarity to rename a new signature according to cosmic v3.2"
  ),
  make_option(
    "--output_spectrum",
    default = NA,
    type = "character",

    # (For larger datasets it is wise to perform more iterations by changing the nrun parameter
    # to achieve stability and avoid local minima)
    nmf_res <- extract_signatures(pseudo_mut_mat, rank = opt$newsignum, nrun = opt$nrun)
    # Assign signature COSMICv3.2 names
    cosmic_signatures <- get_known_signatures()
    nmf_res <- rename_nmf_signatures(nmf_res, cosmic_signatures, cutoff = opt$cosmic_id_threshold)
    sim_matrix <- cos_sim_matrix(cosmic_signatures, nmf_res$signatures)
    plot_cosine_sim <- plot_cosine_heatmap(sim_matrix)
    grid.arrange(plot_cosine_sim)
    # Plot the 96-profile of the signatures:
    p5 <- plot_96_profile(nmf_res$signatures, condensed = TRUE)
    grid.arrange(p5)
    # write matrix of deno signatures for user
    new_sig_matrix <- reshape2::dcast(p5$data, substitution + context ~ sample, value.var = "freq")
    new_sig_matrix <- format(new_sig_matrix, scientific = TRUE)
    newcol <- paste0(gsub("\\..", "", new_sig_matrix$context, perl = TRUE),
                     "[", new_sig_matrix$substitution, "]",
                     gsub("^.\\.", "", new_sig_matrix$context, perl = TRUE))
    new_sig_matrix <- cbind(Type = newcol, new_sig_matrix[, seq_along(new_sig_matrix)[-c(1, 2)]])
    write.table(new_sig_matrix, file = opt$sigmatrix, quote = FALSE, row.names = FALSE, sep = "\t")
    # Visualize the contribution of the signatures in a barplot
    pc1 <- plot_contribution(nmf_res$contribution, nmf_res$signature, mode = "relative", coord_flip = TRUE)
    # Visualize the contribution of the signatures in absolute number of mutations
    pc2 <- plot_contribution(nmf_res$contribution, nmf_res$signature, mode = "absolute", coord_flip = TRUE)
    # Combine the two plots:

    # The relative contribution of each signature for each sample can also be plotted as a heatmap with
    # plot_contribution_heatmap, which might be easier to interpret and compare than stacked barplots.
    # The samples can be hierarchically clustered based on their euclidean dis- tance. The signatures
    # can be plotted in a user-specified order.
    # Plot signature contribution as a heatmap with sample clustering dendrogram and a specified signature order:
    pch1 <- plot_contribution_heatmap(nmf_res$contribution, cluster_samples = TRUE)
    # Plot signature contribution as a heatmap without sample clustering:
    pch2 <- plot_contribution_heatmap(nmf_res$contribution, cluster_samples = FALSE)
    #Combine the plots into one figure:
    grid.arrange(pch1, pch2, ncol = 2, widths = c(2, 1.6))

    # Compare the reconstructed mutational profile with the original mutational profile:
    pch3 <- plot_original_vs_reconstructed(pseudo_mut_mat, nmf_res$reconstructed, y_intercept = 0.95)
    grid.arrange(pch3)
    dev.off()
    }

##### Section 3: Find optimal contribution of known signatures: COSMIC or OWN mutational signatures ####