BioGSP (version 1.0.0)

visualize_similarity_xy: Visualize similarity in low vs non-low frequency space

Description

Create a scatter plot with low-frequency similarity (c_low) on x-axis and non-low-frequency similarity (c_nonlow) on y-axis from runSGCC results

Usage

visualize_similarity_xy(
  similarity_results,
  point_size = 2,
  point_color = "steelblue",
  add_diagonal = TRUE,
  add_axes_lines = TRUE,
  title = "Low-frequency vs Non-low-frequency Similarity",
  show_labels = FALSE,
  show_names = FALSE
)

Value

ggplot object showing similarity space visualization

Arguments

similarity_results

List of similarity results from runSGCC function, or a single result

point_size

Size of points in the plot (default: 2)

point_color

Color of points (default: "steelblue")

add_diagonal

Whether to add diagonal reference lines (default: TRUE)

add_axes_lines

Whether to add x=0 and y=0 reference lines (default: TRUE)

title

Plot title (default: "Low-frequency vs Non-low-frequency Similarity")

show_labels

Whether to show point labels if names are available (default: FALSE)

show_names

Whether to display data point names as text labels using ggrepel (default: FALSE). If more than 50 points, randomly samples 50 for labeling. Requires ggrepel package.

Examples

# \donttest{
# Create example data and compute SGWT
data <- data.frame(x = runif(100), y = runif(100),
                  signal1 = rnorm(100), signal2 = rnorm(100))
SG <- initSGWT(data, signals = c("signal1", "signal2"))
SG <- runSpecGraph(SG, k = 15)
SG <- runSGWT(SG)

# Single similarity result
sim_result <- runSGCC("signal1", "signal2", SG = SG)
plot <- visualize_similarity_xy(sim_result)
print(plot)

# Multiple similarity results (create two different analyses)
data2 <- data.frame(x = runif(100), y = runif(100),
                   signal1 = rnorm(100), signal2 = rnorm(100))
SG2 <- initSGWT(data2, signals = c("signal1", "signal2"))
SG2 <- runSpecGraph(SG2, k = 15)
SG2 <- runSGWT(SG2)

sim_results <- list(
  pair1 = runSGCC("signal1", "signal2", SG = SG),
  pair2 = runSGCC("signal1", "signal2", SG = SG2)
)
plot <- visualize_similarity_xy(sim_results, show_names = TRUE)
print(plot)
# }