PomaCorr: Correlation Analysis

In pcastellanoescuder/POMA: Tools for Omics Data Analysis

Correlation Analysis

Description

PomaCorr computes all pairwise correlations in the data and generates a correlation plot.

Usage

PomaCorr(
  data,
  method = "pearson",
  cluster = TRUE,
  corrplot_shape = "square",
  sig_level = 1
)

Arguments

data	A SummarizedExperiment object.
method	Character. Indicates which correlation coefficient has to be computed. Options are "pearson" (default), "kendall", and "spearman".
cluster	Logical. Indicates whether the correlation plot will be ordered using the hclust function. Default is TRUE.
corrplot_shape	Character. Indicates the visualization method of the correlation plot to be used. Allowed values are "square" (default) and "circle".
sig_level	Numeric. Indicates the significance level. If the correlation p-value exceeds this threshold, the corresponding correlation coefficient is considered insignificant, and that pair will be hidden in the correlation plot. The default is 1, meaning all correlations are included in the plot. For datasets with more than 500 features, this threshold is ignored, and all pairwise correlations are displayed in the plot.

Value

A list with the results.

Author(s)

Pol Castellano-Escuder

Examples

## Output is a list with objects `correlations` (tibble) and `corrplot` (ggplot2 object)
data <- POMA::st000284 # Example SummarizedExperiment object included in POMA

data %>% 
  PomaCorr(method = "pearson")

pcastellanoescuder/POMA documentation built on Oct. 13, 2024, 8:46 p.m.