integration_alluvial_plot {ISAnalytics}

R Documentation

Alluvial plots for IS distribution in time.

Description

Alluvial plots allow the visualization of integration sites distribution in different points in time in the same group. This functionality requires the suggested package ggalluvial.

Usage

integration_alluvial_plot(
  x,
  group = c("SubjectID", "CellMarker", "Tissue"),
  plot_x = "TimePoint",
  plot_y = "fragmentEstimate_sum_PercAbundance",
  alluvia = mandatory_IS_vars(),
  alluvia_plot_y_threshold = 1,
  top_abundant_tbl = TRUE,
  ...
)

Arguments

x	A data frame. See details.
group	Character vector containing the column names that identify unique groups.
plot_x	Column name to plot on the x axis
plot_y	Column name to plot on the y axis
alluvia	Character vector of column names that uniquely identify alluvia
alluvia_plot_y_threshold	Numeric value. Everything below this threshold on y will be plotted in grey and aggregated. See details.
top_abundant_tbl	Logical. Produce the summary top abundant tables via top_abund_tableGrob?
...	Additional arguments to pass on to top_abund_tableGrob

Details

Input data frame

The input data frame must contain all the columns specified in the arguments group, plot_x, plot_y and alluvia. The standard input for this function is the data frame obtained via the compute_abundance function.

Plotting threshold on y

The plotting threshold on the quantification on the y axis has the function to highlight only relevant information on the plot and reduce computation time. The default value is 1, that acts on the default column plotted on the y axis which holds a percentage value. This translates in natural language roughly as "highlight with colors only those integrations (alluvia) that at least in 1 point in time have an abundance value >= 1 %". The remaining integrations will be plotted as transparent in the strata.

Value

For each group a list with the associated plot and optionally the summary tableGrob

See Also

Other Plotting functions: CIS_volcano_plot(), HSC_population_plot(), circos_genomic_density(), sharing_heatmap(), sharing_venn(), top_abund_tableGrob()

Examples

data("integration_matrices", package = "ISAnalytics")
data("association_file", package = "ISAnalytics")
aggreg <- aggregate_values_by_key(
    x = integration_matrices,
    association_file = association_file,
    value_cols = c("seqCount", "fragmentEstimate")
)
abund <- compute_abundance(x = aggreg)
alluvial_plots <- integration_alluvial_plot(abund,
    alluvia_plot_y_threshold = 0.5
)
ex_plot <- alluvial_plots[[1]]$plot +
    ggplot2::labs(
        title = "IS distribution over time",
        subtitle = "Patient 1, MNC BM",
        y = "Abundance (%)",
        x = "Time point (days after GT)"
    )
print(ex_plot)

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