BuildConfounderMap: BuildConfounderMap

In metadeconfoundR: Covariate-Sensitive Analysis of Cross-Sectional High-Dimensional Data

BuildConfounderMap

Description

BuildConfounderMap summarizes confounder analysis of a MetaDeconfound output in a circle plot

Usage

BuildConfounderMap(
  metaDeconfOutput,
  q_cutoff = 0.1,
  featureColor = c("black"),
  featureNames = NULL,
  metaVariableNames = NULL,
  d_col = c("blue", "white", "red"),
  d_range = "full",
  trusted = c("OK_sd", "OK_nc", "OK_d", "AD")
)

Arguments

metaDeconfOutput	output of a metadeconfound run
q_cutoff	optional FDR-value cutoff used to remove low-significance entries from data
featureColor	optional vector of colors named after each unique feature in metaDeconfOutput
featureNames	optional two-column-dataframe containing corresponding "human-readable" names to the "machine-readable" feature names used as row.names in metaDeconfOutput. These human readable names will be displayed in the final plot. First column: machine-readable, second column: human-readable.
metaVariableNames	optional two-column-dataframe containing corresponding "human-readable" names to the "machine-readable" metadata names used as column names in metaDeconfOutput. These human readable names will be displayed in the final plot. First column: machine-readable, second column: human-readable.
d_col	set color range for effect size as c(minimum, middle, maximum), default c("red", "white", "blue")
d_range	range of effect size colors shown; "full": (default) range from -1 to +1 (best for comparability between multiple plots); "fit": range reduced according to maximum and minimum effect size present in resulting plot (better color resolution for weaker effects)
trusted	character vector of confounding status labels to be treated as trustworthy, not-confounded signal. default = c("OK_sd", "OK_nc", "OK_d", "AD")

Details

for more details and explanations please see the package vignette.

Value

list of ggplot2 objects

Author(s)

Kilian Dahm

Examples

data(reduced_feature)
data(metaMatMetformin)

example_output <- MetaDeconfound(featureMat = reduced_feature,
                                  metaMat = metaMatMetformin,
                                  logLevel = "ERROR")

plotObject <- BuildConfounderMap(example_output)
library(ggraph)
plotObject$MS0001

metadeconfoundR documentation built on Feb. 4, 2026, 5:14 p.m.