plotCorrelationMatrix: Plot Correlation Matrix

In MarcElosua/SPOTlight: `SPOTlight`: Spatial Transcriptomics Deconvolution

Plot Correlation Matrix

Description

This function takes in a matrix with the predicted proportions for each spot and returns a correlation matrix between cell types.

Usage

plotCorrelationMatrix(
  x,
  cor.method = c("pearson", "kendall", "spearman"),
  insig = c("blank", "pch"),
  colors = c("#6D9EC1", "white", "#E46726"),
  hc.order = TRUE,
  p.mat = TRUE,
  ...
)

Arguments

x	numeric matrix with rows = samples and columns = cell types Must have at least two rows and two columns.
cor.method	Method to use for correlation: c("pearson", "kendall", "spearman"). By default pearson.
insig	character, specialized insignificant correlation coefficients, "pch", "blank" (default). If "blank", wipe away the corresponding glyphs; if "pch", add characters (see pch for details) on corresponding glyphs.
colors	character vector with three colors indicating the lower, mid, and high color. By default c("#6D9EC1", "white", "#E46726").
hc.order	logical value. If TRUE, correlation matrix will be hc.ordered using hclust function.
p.mat	logical value. If TRUE (default), correlation significance will be used. If FALSE arguments sig.level, insig, pch, pch.col, pch.cex are invalid.
...	additional graphical parameters passed to ggcorrplot.

Value

ggplot object

Author(s)

Marc Elosua Bayes & Helena L Crowell

Examples

set.seed(321)
x <- replicate(m <- 25, runif(10, 0, 1))
rownames(x) <- paste0("spot", seq_len(nrow(x)))
colnames(x) <- paste0("type", seq_len(ncol(x)))

# The most basic example
plotCorrelationMatrix(x = x)

# Showing the non-significant correlatinos
plotCorrelationMatrix(x = x, insig = "pch")

# A more elaborated
plotCorrelationMatrix(
    x = x,
    hc.order = FALSE,
    type = "lower",
    outline.col = "lightgrey",
    method = "circle",
    colors = c("#64ccc9", "#b860bd", "#e3345d"))

MarcElosua/SPOTlight documentation built on March 7, 2024, 4:58 p.m.