ROCh {RQdeltaCT}

ROCh

Description

This function is designed to perform Receiver Operating Characteristic (ROC) analysis based on the gene expression data. This kind of analysis is useful to further examine performance of samples classification into two groups.

Usage

ROCh(
  data,
  sel.Gene = "all",
  groups,
  panels.row,
  panels.col,
  text.size = 1.1,
  print.auc = TRUE,
  print.auc.size = 0.8,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "ROC_plot",
  save.to.txt = FALSE,
  name.txt = "ROC_results"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions.

sel.Gene

Character vector with names of genes to include, or "all" (default) to use all genes.

groups

Character vector length of two with names of two compared groups.

panels.row, panels.col

Integer: number of rows and columns to arrange panels with plots.

text.size

Numeric: size of text on the plot. Default to 1.1.

print.auc

Logical: if TRUE, AUC values with confidence interval will be added to the plot. Default to TRUE.

print.auc.size

Numeric: size of AUC text on the plot. Default to 0.8.

save.to.tiff

Logical: if TRUE, plot will be saved as .tiff file. Default to FALSE.

dpi

Integer: resolution of saved .tiff file. Default to 600.

width

Numeric: width (in cm) of saved .tiff file. Default to 15.

height

Numeric: height (in cm) of saved .tiff file. Default to 15.

name.tiff

Character: name of saved .tiff file, without ".tiff" name of extension. Default to "ROC_plot".

save.to.txt

Logical: if TRUE, returned table with results will be saved to .txt file. Default to FALSE.

name.txt

Character: name of saved .txt file, without ".txt" name of extension. Default to "ROC_results".

Value

Data frame with ROC parameters including AUC, threshold, specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and Youden's J statistic. Plot with ROC curves can be saved to .tiff file and opened from the working directory (will not be displayed on the graphic device).

Examples

library(tidyverse)
library(pROC)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("Gene2","Gene5","Gene6","Gene9","Gene11"),
                      remove.Sample = c("Control08","Control16","Control22"))
data.CtF.ready <- make_Ct_ready(data.CtF, imput.by.mean.within.groups = TRUE)
data.dCt <- delta_Ct(data.CtF.ready, ref = "Gene8")
data.dCtF <- filter_transformed_data(data.dCt, remove.Sample = c("Control11"))
roc_parameters <- ROCh(data.dCtF, sel.Gene = c("Gene1","Gene16","Gene19","Gene20"),
                        groups = c("Disease","Control"),
                        panels.row = 2,
                        panels.col = 2)