plot_effect_heatmap: Draw a heatmap of effects between variables, such as...

In antonvsdata/notame: Workflow for non-targeted LC-MS metabolic profiling

Draw a heatmap of effects between variables, such as correlations

Description

Draws a heatmap of e.g. correlations between variables (see perform_correlation_tests). It is possible to draw only the lower triangular of the heatmap, order rows and columns with hierarchical clustering, and add circles for p-values.

Usage

plot_effect_heatmap(
  data,
  x,
  y,
  effect,
  p = NULL,
  p_limit = 0.1,
  point_size_range = c(1, 6),
  log2_effect = FALSE,
  discretize_effect = FALSE,
  breaks = 5,
  clustering = TRUE,
  dist_method = "euclidean",
  clust_method = "ward.D2",
  lower_tri = FALSE,
  reverse_y = TRUE,
  use_coord_fixed = TRUE,
  symmetric_aspect_ratio = TRUE,
  title = NULL,
  subtitle = NULL,
  fill_scale = NA
)

Arguments

data	a data frame with x and y variables and the effect (used to fill the tiles)
x, y	the column names of data with the x and y variables
effect	the column name of the effect, e.g. correlation
p	optional, the column name with p-values. If provided, points that scale by p-value are drawn on top of the heatmap tiles
p_limit	numeric, only p-values below the limit are plotted as points
point_size_range	a numeric vector of length 2. The upper and lower limits for the point sizes. This needs to be adjusted to make the point size look good when compared to the tiles
log2_effect	logical, whether the effect should be plotted on a logarithmic scale (in case of fold change etc.)
discretize_effect	logical, whether the effect range should be divided into discrete levels instead of using a continuous scale. Can sometimes make patterns more visible, but the hard limits can blur the big picture as well.
breaks	if discretize_effect = TRUE, either the number of breaks or the points where to cut for the levels, see cut
clustering	logical, whether the order of rows and columns should be ordered by hierarchical clustering?
dist_method	distance method used in clustering, see dist
clust_method	clustering method used in clustering, see hclust
lower_tri	logical, should only the lower triangular be plotted?
reverse_y	logical, if clustering = FALSE, lower_tri = FALSE, should the order of the y-axis be reversed so that the diagonal is from top left to bottom right?
use_coord_fixed	logical, should the heatmap tiles be squares? If yes, this uses coord_fixed
symmetric_aspect_ratio	logical, should the plot panel be a square? If yes, uses ggplot2::theme(aspect.ratio = 1).
title, subtitle	the title and subtitle of the plot
fill_scale	fill scale for the heatmap as returned by a ggplot function. Set to NA to choose the appropriate scale based on the class of the effect variable.

Details

All missing effects between variables are replaced by 0 before clustering, since hclust can't deal with missing values.

Value

a ggplot object

See Also

cut for discretizing the effect, dist for distance calculation for clustering, hclust for hierarchical clustering.

Examples

# Compute correlations between variables
correlations <- perform_correlation_tests(example_set,
  x = featureNames(example_set),
  duplicates = TRUE
)

# Minimal example
plot_effect_heatmap(correlations, x = "X", y = "Y", effect = "Correlation_coefficient")

# Lower triangular with discrete effect and p-value dots
plot_effect_heatmap(correlations,
  x = "X", y = "Y", effect = "Correlation_coefficient",
  p = "Correlation_P", point_size_range = c(2, 8),
  discretize_effect = TRUE, breaks = 7, lower_tri = TRUE
)

antonvsdata/notame documentation built on Sept. 14, 2024, 11:09 p.m.