DoseRider: A multi-omics approach to study dose-response relationships at the pathway level using mixed models
DoseRider enhances toxicogenomics by employing mixed models with cubic splines for the analysis of nonlinear dose-response relationships at the pathway level. This methodology is suitable for multi-omics research and accessible both as an R package and web application. It determines pathway trends and calculates Trend Change Doses (TCD) and Benchmark Doses (BMD). DoseRider uncovers essential dose-response pathways and molecular patterns, improving insights into the effects of compounds or drugs at varying doses.
- Non-linear and Linear Modeling: Implements linear mixed models with cubic splines and generalized mixed models to accommodate non-linear dose-response relationships.
- Pathway-Level Dose-Response Modeling: Models dose-response at the pathway or gene-set level, calculating TCDs and BMDs.
- Multi-Omics Support: Compatible with various omics data types, including RNA-Seq and metabolomics.
- Parallel Computing: Utilizes parallel processing for efficient handling of large-scale datasets.
- Visualization Tools: Multiple built-in plotting functions to visualize dose-response trends and model outputs.
- Customizable Gene Sets: Filter and analyze custom or preprocessed gene sets.
If you use DoseRider in your research, please cite:
Monfort-Lanzas, P., Gostner, J. M., & Hackl, H. (2025). Modeling omics dose-response at the pathway level with doserider. Computational and Structural Biotechnology Journal, 27, 1440–1448. https://doi.org/10.1016/j.csbj.2025.04.004
To install the latest development version from GitHub:
# install.packages("devtools")
devtools::install_github("icbi-lab/doseRider")Below is a simple example demonstrating the use of DoseRider:
# Load DoseRider
library(doseRider)
# Load your gene expression data
data("bpaf_data")
# Load gene sets
gmt_path <- system.file("extdata", "High-Response-Toxicogenomics.gmt", package = "doseRider")
gmt <- read_gmt(gmt_path)
# Perform dose-response analysis
# Run doseRider analysis
dose_rider_results <- DoseRiderParallel(
se = bpaf_data,
gmt = gmt,
dose_col = "Dose",
omic = "rnaseq",
minGSsize = 20,
maxGSsize = 200,
method = "fdr",
covariates = c(),
modelType = "LMM",
num_cores = 10,
FilterPathway = TRUE,
log_transform = TRUE,
models = c("linear", "non_linear_mixed")
)DoseRider provides a comprehensive suite of visualizations to explore dose-response relationships at both the dataset and pathway levels. Visual outputs are organized below and are automatically generated for each analysis.
These plots summarize dose-response behavior across all significant pathways:
Ranks top pathways by -log₁₀(adjusted p-value). Dot size and position reflect significance and pathway size.
p1 <- plot_dotplot_top_pathways(dose_rider_results_filter, top = 10)
Visualizes average expression across doses for top pathways. Intensity shows magnitude of response.
p2 <- dose_response_heatmap(dose_rider_results_filter, dose_col = "Dose", top = 10)
Displays gene-specific deviations within each pathway using mixed-model random effects.
p3 <- plot_gene_set_random_effects(dose_rider_results_filter, dose_col = "log_Dose", top = 10)
Shows modeled expression curves for top pathways based on spline fits.
p4 <- plot_top_pathway_responses(dose_rider_results_filter, top = 8, dose_col = "log_Dose", clusterResults = TRUE)
Displays the distribution of Benchmark Dose (BMD) values across pathways, highlighting sensitive dose regions.
data_bmd <- get_bmd_range(dose_rider_results_filter)
p5 <- plot_bmd_density_and_peaks(data_bmd)
Plots the distribution of Trend Change Doses (TCDs), where expression curves shift direction.
data_tcd <- get_tcd_range(dose_rider_results_filter)
p6 <- plot_tcd_density(data_tcd)
Bootstrap-based 95% confidence intervals for BMD values of top pathways.
p7 <- plot_bmd_confidence_intervals(head(bmd_bounds_df, 20))
Bootstrap-based 95% confidence intervals for TCD1 values of top pathways.
p8 <- plot_tcd1_confidence_intervals(bmd_bounds_df)
These plots give insight into individual pathways.
Shows expression variability and random effect strength for a selected pathway.
p9 <- plot_gene_random_effect_relationship(dose_rider_results_filter, "Estrogen signaling pathway - Homo sapiens (human)")
Heatmap of individual gene expression across doses for a selected pathway.
p10 <- create_gene_heatmap(dose_rider_results_filter, gene_set_name = "Estrogen signaling pathway - Homo sapiens (human)")
The Toxicogenomics Gene Set within DoseRider focuses on pathways that exhibit significant changes across compounds from the TG-GATES database. The score is calculated by multiplying NES by the negative logarithm of the p-value for each compound and dose level. The scores are then averaged across doses to generate Z-score normalized weights.
You can download the GMT file directly from the repository:
📂 Download GMT file
(Path: inst/extdata/High-Response-Toxicogenomics.gmt)
The heatmap below shows weighted scores across TG-GATES compounds, illustrating the most responsive pathways:
Contributions are welcome! If you find any bugs or have suggestions for new features, please open an issue or submit a pull request on our GitHub repository.
DoseRider is licensed under the MIT License. See the LICENSE file for more details.