Oncoplot of TCGA LAML Mutation Data

We utilize the TCGA LAML mutation dataset included in the R maftools package.

To reduce complexity, we randomly sample 20 tumor samples. The data preparation code is initially collapsed in the source but can be expanded for inspection.

Code

# load data from `maftools`
laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
# clinical information containing survival information and histology. This is optional
laml.clin <- system.file("extdata", "tcga_laml_annot.tsv", package = "maftools")
laml <- maftools::read.maf(
    maf = laml.maf,
    clinicalData = laml.clin,
    verbose = FALSE
)
set.seed(25) # 2, 11, 20, 25, 29, 32
new_laml <- maftools::subsetMaf(laml,
    tsb = sample(maftools::getClinicalData(laml)$Tumor_Sample_Barcode, 20)
)
#> -Processing clinical data

Code

library(ggalign)
#> Loading required package: ggplot2
#> 
#> Attaching package: 'ggalign'
#> The following object is masked from 'package:ggplot2':
#> 
#>     element_polygon

ggoncoplot(
    new_laml,
    n_top = 10, collapse_vars = FALSE, filling = FALSE,
    remove_empty_samples = FALSE
) +
    geom_subtile(aes(fill = value),
        ncol = 1L, height = 0.8,
        show.legend = TRUE
    ) +
    theme_no_axes("x") +

    # add top annotation -------------------------------
    anno_top(size = 0.2) +
    ggalign(
        data = function(data) {
            vars <- ggalign_lvls(data)
            data <- ggalign_attr(data, "sample_summary")
            as.matrix(data[intersect(names(data), vars)])
        }
    ) +
    scheme_data(function(data) {
        data$.column_names <- factor(data$.column_names, ggalign_lvls(data))
        data
    }) +
    geom_bar(aes(.x, value, fill = .column_names),
        stat = "identity"
    ) +
    guides(fill = "none") +
    ylab("TMB") +
    scale_y_continuous(breaks = scales::breaks_pretty(2L)) +

    # add right annotation -----------------------------
    anno_right(size = 0.4) -
    # remove bottom spaces of the right annotation when aligning
    scheme_align(free_spaces = "b") +

    # add the text percent for the alterated samples in the right annotation
    ggalign(
        data = function(data) {
            # Atomic vector will be converted into a data frame.
            ggalign_attr(data, "gene_summary")$AlteredSamples /
                ggalign_attr(data, "n_samples")
        }
    ) +
    geom_text(aes(1, label = scales::label_percent()(value)),
        hjust = 1.2, size = 5, family = "Helvetica"
    ) +
    scale_x_continuous(
        expand = expansion(),
        name = NULL, breaks = NULL,
        limits = c(0, 1)
    ) +
    theme(plot.margin = margin()) +

    # add the bar plot in the right annotation
    ggalign(data = function(data) {
        vars <- ggalign_lvls(data)
        data <- ggalign_attr(data, "variant_weights")
        as.matrix(data[intersect(names(data), vars)])
    }) +
    scheme_data(function(data) {
        data$.column_names <- factor(data$.column_names, ggalign_lvls(data))
        data
    }) +
    geom_bar(aes(value, fill = .column_names),
        stat = "identity",
        orientation = "y"
    ) +
    guides(fill = "none") +
    # labs(fill = "right") +
    xlab("Variant weights") +
    scale_x_continuous(breaks = scales::breaks_pretty(1)) -
    # we apply the scale mapping to
    # - the top and right annotation: `position = "tr"`
    # - the main plot: `position = "i"`
    quad_scope(
        scale_fill_brewer(
            "Mutations",
            palette = "Dark2",
            na.translate = FALSE, drop = FALSE
        ),
        position = "tri"
    ) +

    # add bottom annotation ----------------------------
    anno_bottom(size = 0.2) +
    # add bar plot in the bottom annotation
    ggalign(data = function(data) {
        data <- ggalign_attr(data, "titv")$fraction.contribution
        as.matrix(data[-1L])
    }) +
    geom_bar(aes(y = value, fill = .column_names), stat = "identity") +
    ylab("Ti/Tv") +
    scale_y_continuous(breaks = scales::breaks_pretty(3L)) +
    scale_fill_brewer("Ti/Tv", palette = "Set2", na.translate = FALSE)
#> `geom_subtile()` subdivide tile into a maximal of 3 rectangles