heatmap_ic {biotmle}R Documentation

Heatmap for class biotmle

Description

Heatmap of the contributions of a select subset of biomarkers to the variable importance measure changes as assessed by influence curve-based estimation, across all subjects. The heatmap produced performs supervised clustering, in the sense first described in Pollard & van der Laan (2008) <doi:10.2202/1544-6115.1404>.

Usage

heatmap_ic(x, ..., design, FDRcutoff = 0.05, type = c("top", "all"),
  top = 25)

Arguments

x

Object of class biotmle as produced by an appropriate call to biomarkertmle

...

additional arguments passed to superheat::superheat as necessary

design

A vector providing the contrast to be displayed in the heatmap.

FDRcutoff

Cutoff to be used in controlling the False Discovery Rate.

type

A character describing whether to plot only a top number (as defined by FDR-corrected p-value) of biomarkers or all biomarkers.

top

Number of identified biomarkers to plot in the heatmap.

Value

heatmap (from the superheat package) using hierarchical clustering to plot the changes in the variable importance measure for all subjects across a specified top number of biomarkers.

Examples

library(dplyr)
library(biotmleData)
library(SummarizedExperiment)
data(illuminaData)
data(biomarkertmleOut)

colData(illuminaData) <- colData(illuminaData) %>%
  data.frame() %>%
  dplyr::mutate(age = as.numeric(age > median(age))) %>%
  DataFrame()

varInt_index <- which(names(colData(illuminaData)) %in% "benzene")
designVar <- as.data.frame(colData(illuminaData))[, varInt_index]
design <- as.numeric(designVar == max(designVar))

limmaTMLEout <- modtest_ic(biotmle = biomarkerTMLEout)

heatmap_ic(x = limmaTMLEout, design = design, FDRcutoff = 0.05, top = 15)
#

[Package biotmle version 1.10.0 Index]