Create an array of ROC plots with confidence interval bands to compare signatures.

Create an array of ROC plots with confidence interval bands to compare signatures.

Usage

signatureROCplot_CI(
  inputData,
  annotationData,
  signatureColNames,
  annotationColName,
  scale = FALSE,
  choose_colors = c("cornflowerblue", "gray50", "gray79"),
  name = NULL,
  nrow = NULL,
  ncol = NULL,
  ci.lev = 0.95,
  pb.show = TRUE
)

Arguments

inputData

an input data object. It should either be of the class SummarizedExperiment and contain the profiled signature data and annotation data as columns in the colData, or alternatively be of the classes data.frame or matrix and contain only the gene expression data. Required.

annotationData

a data.frame or matrix of annotation data, with one column. Only required if inputData is a data.frame or matrix of signature data.

signatureColNames

a vector of the column names of inputData that contain the signature data. If inputData is a SummarizedExperiment object, these are the column names of the object colData.

annotationColName

a character string naming the column name in the colData that contains the annotation data to be used in making the boxplot. Only required if inputData is a SummarizedExperiment object.

scale

logical. Setting scale = TRUE scales the signature data. The default is FALSE.

choose_colors

a vector of length 3 defining the colors to be used in the ROC plots. The default is c("cornflowerblue", "gray50", "gray79").

name

a character string giving the title of the ROC plot. If NULL, the plot title will be "ROC Plots for Gene Signatures, <ci.lev>% Confidence". The default is NULL.

nrow

integer giving the number of rows in the resulting array.

ncol

integer giving the number of columns in the resulting array.

ci.lev

a number between 0 and 1 giving the desired level of confidence for computing ROC curve estimations.

pb.show

logical for whether to show a progress bar while running code. The default is TRUE.

Value

An array of ROC plots.

Examples

# Run signature profiling

 choose_sigs <- TBsignatures[c(1, 2)]
 prof_indian <- runTBsigProfiler(TB_indian, useAssay = "logcounts",
                                 algorithm = "Zscore",
                                 signatures = choose_sigs,
                                 parallel.sz = 1)
#> Parameter update_genes is TRUE. Gene names will be updated.
#> Running Zscore
#> ! 1 genes with constant values throughout the samples
#> ! Genes with constant values are discarded

# Create ROC plots with confidence intervals
signatureROCplot_CI(prof_indian, signatureColNames = names(choose_sigs),
                    annotationColName = "label")
#> 
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