cor_plot: Correlation Plot with Significance Points

In iPRISM: Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling

Correlation Plot with Significance Points

Description

This function generates a correlation plot between two datasets, displaying correlation coefficients as a heatmap and significant correlations as scatter points.

Usage

cor_plot(
  data1,
  data2,
  sig.name1 = "value1",
  sig.name2 = "value2",
  cutoff.pvalue = 0.05,
  color = c("#62CCC9", "#FF9999")
)

Arguments

data1	A data frame or matrix representing the first dataset.
data2	A data frame or matrix representing the second dataset.
sig.name1	A character string specifying the name of the first dataset (default: "value1").
sig.name2	A character string specifying the name of the second dataset (default: "value2").
cutoff.pvalue	The significance threshold for correlation (default: 0.05).
color	A vector of two colors for the heatmap gradient (default: c("#62CCC9", "#FF9999")).

Details

The function computes correlation coefficients between corresponding columns in the two datasets and identifies significant correlations based on p-values.

Value

A ggplot object displaying the correlation heatmap and scatter points.

Examples

# Read all data into memory
data(data.path, package = "iPRISM")
data(data.cell, package = "iPRISM")
# Draw the plot
cor_plot(data1 = data.path,data2 = data.cell,sig.name1 = "path",sig.name2 = "cell")

iPRISM documentation built on Sept. 11, 2024, 7:14 p.m.