Title:	Creating Volcano Plots from Bayesian Model Posteriors
Version:	1.0.1
URL:	https://github.com/KatjaDanielzik/BayesVolcano
BugReports:	https://github.com/KatjaDanielzik/BayesVolcano/issues
Description:	Bayesian models are used to estimate effect sizes (e.g., gene expression changes, protein abundance differences, drug response effects) while accounting for uncertainty, small sample sizes, and complex experimental designs. However, Bayesian posteriors of models with many parameters are often difficult to interpret at a glance. One way to quickly identify important biological changes based on frequentist analysis are volcano plots (using fold-changes and p-values). Bayesian volcano plots bring together the explicit treatment of uncertainty in Bayesian models and the familiar visualization of volcano plots.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.5)
Imports:	ggplot2, HDInterval, purrr, dplyr, magrittr, tidyr
Suggests:	brms, rstan, knitr, rmarkdown, testthat (≥ 3.0.0)
LazyData:	true
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2026-03-26 14:08:41 UTC; katja
Author:	Katja Danielzik [aut, cre, cph], Simo Kitanovski [aut, ctb], Daniel Hoffmann [aut]
Maintainer:	Katja Danielzik <katja.danielzik@uni-due.de>
Repository:	CRAN
Date/Publication:	2026-03-31 09:50:15 UTC

CrI width

Description

CrI width

Usage

.CrI.width(CrI.low, CrI.high)

Arguments

Value

absolute distance lower and upper bound of CrI

pi_value

Description

Calculates pi-value as integral under

Usage

.pi_value(value, null.effect)

Arguments

Value

pi-value

A posterior with parameter "delta_mu"

Description

A data with two columns "parameter" and "label" corresponding to data("posterior")

Usage

data("posterior")

Format

"Parameter" refers to column names of posterior "Lable" are the biological entities corresponding to the parameters

Source

Script used to create simulated data #BiocManager::install("MetaboDynamics") library(MetaboDynamics) data("longitudinalMetabolomics_df") library(BayesVolcano) data("posterior")

annotation_df <- as.data.frame(cbind(parameter=colnames(posterior), label=levels(as.factor(longitudinalMetabolomics_df$metabolite)), group=rep(c("A","B")),value=rnorm(98,10,3))) annotation_df$value <- as.numeric(annotation_df$value)

extract_fit

Description

Wrapper function to extract parameter draws from two common Stan interfaces. This function requires the respective stan interface (rstan, brms) package to be installed.

Usage

extract_fit(fit, parameter_name)

Arguments

Value

A data frame with one row per MCMC draw and one column per parameter. If multiple parameters, columns are named after the parameter.

Examples

# Not run:
# fit <- brms::brm(count ~ zAge + zBase * Trt + (1|patient),
#             data = brms::epilepsy[1:30,], family = poisson())

# posterior <- extract_stan_fit(fit, "b_Intercept")
# End(Not run)

Plot Bayesian Volcano plot

Description

Plot Bayesian Volcano plot

Usage

plot_volcano(result, CrI = FALSE, CrI_width = FALSE, color = NULL)

Arguments

Value

a ggplot2 object

Examples

data("posterior")
head(posterior)
data("annotation_df")
head(annotation_df)

result <- prepare_volcano_input(
  posterior = posterior,
  annotation = annotation_df,
)
plot_volcano(result,
  color = "group",
  CrI = TRUE,
  CrI_width = TRUE
)

A posterior with parameter "delta_mu"

Description

A data frame with 98 columns (each indicating posterior samples of the paramter "delta_mu" of one biological entity like e.g. a metabolite) and 1000 rows (the number of total samples (chains*iterations)) of the Bayesian model.

Usage

data("posterior")

Format

A data frame with 98 columns (each indicating posterior samples of the paramter "delta_mu" of one biological entity like e.g. a metabolite) and 1000 rows (the number of total samples (chains*iterations)) of the Bayesian model.

Source

Script used to create simulated data #BiocManager::install("MetaboDynamics") library(MetaboDynamics) data("longitudinalMetabolomics_df") fit_dynamics_model(data=longitudinalMetabolomics_df) posterior <- as.data.frame(rstan::extract(fit,pars="euclidean_distance"))

library(stringr) cols <- colnames(posterior)[str_detect(pattern = ".1.2",string=colnames(posterior))] posterior <- posterior[,cols] posterior <- posterior[,-c(1:10,109:118)]

Prepare volcano input

Description

This function has as input posterior draws, calculates pi-values and credible intervals (CrI), and annotates them with biological information (e.g., cell line, time point) based on parameter names and a user-provided annotation data frame. Returns a data frame that is ready for plotting.

Usage

prepare_volcano_input(posterior, annotation, null.effect = 0, CrI_level = 0.95)

Arguments

Details

Only returns pi-values and credible intervals for parameters that are in posterior and annotation. For formula see README or Vignette

Value

A list with:

• result: A data frame with columns:

  • parameter: original parameter name

  • pi.value: calculated pi.value

  • null.effect: set null effect by user

  • parameter.median: median posterior parameter value

  • parameter.low: lower boundary of CrI of parameter value

  • paramter.high: upper boundary of CrI of parameter value

  • CrI.width: the absolute distance between parameter.low and parameter.high

  • CrI.level: CrI_level set by user

  • label: biological label (e.g., cell.line)

  • Other columns from annotation (e.g., group, condition)

See Also

extract_fit()

Examples

# Example: Simulate posterior and annotation
posterior <- data.frame(
  doubling.1 = rnorm(1000),
  doubling.2 = rnorm(1000)
)

annotation <- data.frame(
  parameter = c("doubling.1", "doubling.2"),
  label = c("cell.line.A", "cell.line.B"),
  group = c("group1", "group1")
)

result <- prepare_volcano_input(
  posterior = posterior,
  annotation = annotation,
)

head(result$result)