correlations: Feature correlation analysis

In jasenfinch/metabolyseR: Methods for Pre-Treatment, Data Mining and Correlation Analyses of Metabolomics Data

Feature correlation analysis

Description

Feature correlation analysis.

Usage

correlations(d, ...)

## S4 method for signature 'AnalysisData'
correlations(
  d,
  method = "pearson",
  pAdjustMethod = "bonferroni",
  corPvalue = 0.05,
  minCoef = 0,
  maxCor = Inf
)

## S4 method for signature 'Analysis'
correlations(d)

Arguments

d	S4 object of class AnalysisData
...	arguments to pass to specific method
method	correlation method. One of pearson or spearman.
pAdjustMethod	p-value adjustment method. See ?p.adjust for available methods.
corPvalue	p-value cut-off threshold for significance
minCoef	minimum absolute correlation coefficient threshold
maxCor	maximum number of returned correlations

Details

Correlation analyses can be used to identify associated features within data sets. This can be useful to identifying clusters of related features that can be used to annotate metabolites within data sets. All features are compared and the returned table of correlations are thresholded to the specified p-value cut-off.

Value

A tibble containing results of significantly correlated features.

Examples

library(metaboData)

d <- analysisData(abr1$neg[,200:300],abr1$fact)

correlations(d)

jasenfinch/metabolyseR documentation built on Sept. 18, 2023, 1:25 a.m.