Plot Correlation of Summary Scores as Heatmap

This function generates a ComplexHeatmap object.

Usage

heatmapCorSigScores(
  data,
  cor = NULL,
  scores = NULL,
  name = "test\nstatistic",
  column_title = "Correlation",
  col = NULL,
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  ...
)

Arguments

data

data frame, output of computeSigScores

cor

(optional) matrix containing the data to plot. If missing, a correlation matrix is internally computed using the default settings of function computeSigScoresCorrelation

scores

(optional) character vector, indicating the summary score(s) to plot from data

name

Name of the heatmap. By default the heatmap name is used as the title of the heatmap legend.

column_title

Title on the column.

col

A vector of colors if the color mapping is discrete or a color mapping function if the matrix is continuous numbers (should be generated by colorRamp2). If the matrix is continuous, the value can also be a vector of colors so that colors can be interpolated. Pass to ColorMapping. For more details and examples, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#colors .

cluster_rows

If the value is a logical, it controls whether to make cluster on rows. The value can also be a hclust or a dendrogram which already contains clustering. Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#clustering .

cluster_columns

Whether make cluster on columns? Same settings as cluster_rows.

...

further arguments to internal function call

Value

A ComplexHeatmap object.

See also

computeSigScoresCorrelation, heatmapSigScores

Author

Alessandro Barberis

Examples

#Set seed for reproducibility
set.seed(seed = 5381L)

#Define row/col size
n = 10

#Create input matrix
x = matrix(
 data = stats::runif(n = n*n, min = 0, max = 1000),
 nrow = n,
 ncol = n,
 dimnames = list(
    paste0("g",seq(n)),
    paste0("S",seq(n))
 )
)

#Compute 5 Summary Scores
x = computeSigScores(
 x = x,
 i = rownames(x),
 scores = c("mean", "median", "mode", "midrange", "midhinge")
)

#Plot correlation (correlation is internally computed)
heatmapCorSigScores(data = x)





#Use simulated correlation matrix

#Define row/col size
n = 5

#Create matrix
x = matrix(
 data = 1,
 nrow = n,
 ncol = n,
 dimnames = list(
   getAvailableScores()$id[1:n],
   getAvailableScores()$id[1:n]
 )
)

#Create data
data = stats::runif(n = (((n*n)-n) / 2), min = -1, max = 1)

#Update upper triangular
x[upper.tri(x = x, diag = FALSE)] = data

#Update lower triangular to be symmetric
x[lower.tri(x = x, diag = FALSE)] = t(x)[lower.tri(x)]

#Plot
heatmapCorSigScores(cor = x)