Plot visualization plot from a ColocBoost output. — colocboost

colocboost_plot generates visualization plots for colocalization events from a ColocBoost analysis.

Usage

colocboost_plot(
  cb_output,
  y = "log10p",
  grange = NULL,
  plot_cos_idx = NULL,
  outcome_idx = NULL,
  plot_all_outcome = FALSE,
  plot_focal_only = FALSE,
  plot_focal_cos_outcome_only = FALSE,
  points_color = "grey80",
  cos_color = NULL,
  add_vertical = FALSE,
  add_vertical_idx = NULL,
  outcome_names = NULL,
  plot_cols = 2,
  variant_coord = FALSE,
  show_top_variables = FALSE,
  show_cos_to_uncoloc = FALSE,
  show_cos_to_uncoloc_idx = NULL,
  show_cos_to_uncoloc_outcome = NULL,
  plot_ucos = FALSE,
  plot_ucos_idx = NULL,
  title_specific = NULL,
  ylim_each = TRUE,
  outcome_legend_pos = "top",
  outcome_legend_size = 1.8,
  cos_legend_pos = c(0.05, 0.4),
  show_variable = FALSE,
  lab_style = c(2, 1),
  axis_style = c(2, 1),
  title_style = c(2.5, 2),
  ...
)

Source

See detailed instructions in our tutorial portal: https://statfungen.github.io/colocboost/articles/Visualization_ColocBoost_Output.html

Arguments

cb_output: Output object from colocboost analysis
y: Specifies the y-axis values, default is "log10p" for -log10 transformed marginal association p-values.
grange: Optional plotting range of x-axis to zoom in to a specific region.
plot_cos_idx: Optional indices of CoS to plot
outcome_idx: Optional indices of outcomes to include in the plot. outcome_idx=NULL to plot only the outcomes having colocalization.
plot_all_outcome: Optional to plot all outcome in the same figure.
plot_focal_only: Logical, if TRUE only plots colocalization with focal outcome, default is FALSE.
plot_focal_cos_outcome_only: Logical, if TRUE only plots colocalization including at least on colocalized outcome with focal outcome, default is FALSE.
points_color: Background color for non-colocalized variables, default is "grey80".
cos_color: Optional custom colors for CoS.
add_vertical: Logical, if TRUE adds vertical lines at specified positions, default is FALSE
add_vertical_idx: Optional indices for vertical lines.
outcome_names: Optional vector of outcomes names for the subtitle of each figure. outcome_names=NULL for the outcome name shown in data_info.
plot_cols: Number of columns in the plot grid, default is 2. If you have many colocalization. please consider increasing this.
variant_coord: Logical, if TRUE uses variant coordinates on x-axis, default is FALSE. This is required the variable names including position information.
show_top_variables: Logical, if TRUE shows top variables for each CoS, default is FALSE
show_cos_to_uncoloc: Logical, if TRUE shows colocalization to uncolocalized outcomes to diagnose, default is FALSE
show_cos_to_uncoloc_idx: Optional indices for showing CoS to all uncolocalized outcomes
show_cos_to_uncoloc_outcome: Optional outcomes for showing CoS to uncolocalized outcomes
plot_ucos: Logical, if TRUE plots also trait-specific (uncolocalized) sets , default is FALSE
plot_ucos_idx: Optional indices of trait-specific (uncolocalized) sets to plot when included
title_specific: Optional specific title to display in plot title
ylim_each: Logical, if TRUE uses separate y-axis limits for each plot, default is TRUE
outcome_legend_pos: Position for outcome legend, default is "top"
outcome_legend_size: Size for outcome legend text, default is 1.2
cos_legend_pos: Proportion of the legend from (left edge, bottom edge), default as (0.05, 0.4) at the left - median position
show_variable: Logical, if TRUE displays variant IDs, default is FALSE
lab_style: Vector of two numbers for label style (size, boldness), default is c(2, 1)
axis_style: Vector of two numbers for axis style (size, boldness), default is c(2, 1)
title_style: Vector of two numbers for title style (size, boldness), default is c(2.5, 2)
...: Additional parameters passed to plot functions

Value

Visualization plot for each colocalization event.

Examples

# colocboost example
set.seed(1)
N <- 1000
P <- 100
# Generate X with LD structure
sigma <- 0.9^abs(outer(1:P, 1:P, "-"))
X <- MASS::mvrnorm(N, rep(0, P), sigma)
colnames(X) <- paste0("SNP", 1:P)
L <- 3
true_beta <- matrix(0, P, L)
true_beta[10, 1] <- 0.5 # SNP10 affects trait 1
true_beta[10, 2] <- 0.4 # SNP10 also affects trait 2 (colocalized)
true_beta[50, 2] <- 0.3 # SNP50 only affects trait 2
true_beta[80, 3] <- 0.6 # SNP80 only affects trait 3
Y <- matrix(0, N, L)
for (l in 1:L) {
  Y[, l] <- X %*% true_beta[, l] + rnorm(N, 0, 1)
}
res <- colocboost(X = X, Y = Y)
#> Validating input data.
#> Starting gradient boosting algorithm.
#> Gradient boosting for outcome 1 converged after 98 iterations!
#> Gradient boosting for outcome 3 converged after 106 iterations!
#> Gradient boosting for outcome 2 converged after 107 iterations!
#> Performing inference on colocalization events.
colocboost_plot(res, plot_cols = 1)

colocboost_plot(res, plot_cols = 1, outcome_idx = 1:3)