metadeconfoundR (version 1.0.5)

BuildConfounderMap: 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")
)

Value

list of ggplot2 objects

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")

Author

Kilian Dahm

Details

for more details and explanations please see the package vignette.

Examples

data(reduced_feature)
data(metaMatMetformin)
# \donttest{
example_output <- MetaDeconfound(featureMat = reduced_feature,
                                  metaMat = metaMatMetformin,
                                  logLevel = "ERROR")

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