plotPcorvsCor {CoNI}

Correlation vs Partial correlation

Description

This function fits two linear models on standardize data and plots the results. It generates a scatter plot with two regression lines, where the slopes correspond to the correlation and partial correlation coefficients (blue for cor and red for pcor)

Usage

plotPcorvsCor(
  ResultsCoNI,
  edgeFeature,
  vertexD,
  edgeD,
  vertexFeatures = NULL,
  outputDir = "./",
  fname,
  label_edgeFeature = "Edge Feature",
  plot_to_screen = TRUE,
  height = 10,
  width = 8,
  saveFiles = FALSE
)

Arguments

ResultsCoNI

The significant results generated by CoNI

edgeFeature

The edge feature to explore e.g. Fabp2 (for a gene)

vertexD

Vertex data that was given as input to CoNI

edgeD

Edge data that was given as input to CoNI

vertexFeatures

The vertex features to include as a list. If not specified all metabolites available in combination with the edgeFeature will be used

outputDir

Output directory with path

fname

File name to save the plots

label_edgeFeature

Name for plot title e.g. Gene or Protein

plot_to_screen

logical. If TRUE plots will be outputted to the plotting screen

height

height of the plotting area for the saved file

width

width of the plotting are for the saved file

saveFiles

logical. If FALSE plot is not saved to disk

Value

Returns a ggplot object for a scatter plot with two regression lines. The blue line is the regression of the vertex features, and the red line is the regression of the resulting residuals after regressing each vertex feature with the edge feature. The slope of the blue line corresponds to the pearson correlation coefficient and the slope of the red line to the partial correlation coefficient

Examples

#Load gene expression - Toy dataset of two treatments
data(GeneExpToy)
#Samples in rows and genes in columns
GeneExp <- as.data.frame(t(GeneExpToy))
hfd_gene <- GeneExp[1:8,] #high fat diet
chow_gene<- GeneExp[9:nrow(GeneExp),] #chow diet

#Load metabolite expression - Toy dataset of two treatments
data(MetaboExpToy)
MetaboExp <- MetaboExpToy
hfd_metabo <- MetaboExp[11:18,] #high fat diet
chow_metabo <- MetaboExp[1:10,] #chow diet

#Match row names both data sets
rownames(hfd_metabo)<-rownames(hfd_gene)
rownames(chow_metabo)<-rownames(chow_gene)

#Load CoNI results
data(CoNIResultsHFDToy)

plotPcorvsCor(ResultsCoNI = CoNIResultsHFDToy,
              edgeFeature = "Arfrp1",
              vertexFeatures = c("PC.ae.C40.2", "SM..OH..C22.1"),
              vertexD = hfd_metabo,
              edgeD = hfd_gene,
              label_edgeFeature = "Gene",
              plot_to_screen = TRUE,
              height = 10,
              saveFiles = FALSE)