show_cor: A Simple and General Way for Association Analysis

In sigminer: Extract, Analyze and Visualize Mutational Signatures for Genomic Variations

A Simple and General Way for Association Analysis

Description

All variables must be continuous. The matrix will be returned as an element of ggplot object. This is basically a wrapper of R package ggcorrplot.

Usage

show_cor(
  data,
  x_vars = colnames(data),
  y_vars = x_vars,
  cor_method = "spearman",
  vis_method = "square",
  lab = TRUE,
  test = TRUE,
  hc_order = FALSE,
  p_adj = NULL,
  ...
)

Arguments

data	a data.frame.
x_vars	variables/column names shown in x axis.
y_vars	variables/column names shown in y axis.
cor_method	method for correlation, default is 'spearman'.
vis_method	visualization method, default is 'square', can also be 'circle'.
lab	logical value. If TRUE, add correlation coefficient on the plot.
test	if TRUE, run test for correlation and mark significance.
hc_order	logical value. If TRUE, correlation matrix will be hc.ordered using hclust function.
p_adj	p adjust method, see stats::p.adjust for details.
...	other parameters passing to ggcorrplot::ggcorrplot().

Value

a ggplot object

See Also

show_sig_feature_corrplot for specific and more powerful association analysis and visualization.

Examples

data("mtcars")
p1 <- show_cor(mtcars)
p2 <- show_cor(mtcars,
  x_vars = colnames(mtcars)[1:4],
  y_vars = colnames(mtcars)[5:8]
)
p3 <- show_cor(mtcars, vis_method = "circle", p_adj = "fdr")
p1
p1$cor
p2
p3

## Auto detect problem variables
mtcars$xx <- 0L
p4 <- show_cor(mtcars)
p4

sigminer documentation built on May 29, 2024, 3:11 a.m.