SplineOmics

The R package SplineOmics finds the significant features (hits) of time-series -omics data by using splines and limma for hypothesis testing. It then clusters the hits based on the spline shape while showing all results in summary HTML reports.

The graphical abstract below shows the full workflow streamlined by SplineOmics:

Graphical Abstract of SplineOmics Workflow

Table of Contents

• 📘 Introduction
• 🔧 Installation
  • 🐳 Docker Container
• ▶️ Usage
  • 🎓 Tutorial
  • 📋 Details
  • 🧬 RNA-seq and Glycan Data
• 📦 Dependencies
• 📚 Further Reading
• ❓ Getting Help
• 💬 Feedback
• 📜 License
• 🎓 Citation
• 🌟 Contributors
• 🙏 Acknowledgements
• ⭐ Liking this project

📘 Introduction

Welcome to SplineOmics, an R package designed to streamline the analysis of -omics time-series data, followed by automated HTML report generation.

Is the SplineOmics package of use for me?

If you have -omics data over time, the package will help you to run limma with splines, perform the clustering, run ORA and show result plots in HTML reports. Any time-series data that is a valid input to the limma package is also a valid input to the SplineOmics package (such as transcriptomics, proteomics, phosphoproteomics, metabolomics, glycan fractional abundances, etc.).

What do I need precisely?

1. Data: A data matrix where each row is a feature (e.g., protein, metabolite, etc.) and each column is a sample taken at a specific time. The data must have no NA values, should have normally distributed features and no dependence between the samples.

2. Meta: A table with metadata on the columns/samples of the data matrix (e.g., batch, time point, etc.)

3. Annotation (optional): A table with identifiers on the rows/features of the data matrix (e.g., gene and protein name).

Capabilities

With SplineOmics, you can:

• Automatically perform exploratory data analysis:

  The explore_data() function generates an HTML report, containing various plots, such as density, PCA, and correlation heatmap plots (example report).

• Perform limma spline analysis:

  Use the run_limma_splines() function to perform the limma analysis with splines once the optimal hyperparameters are identified (example report).

• Find jumps and drops in the timecourse:

  Use the find_pvc() function for that (example report).

• Cluster significant features:

  Cluster the significant features (hits) identified in the spline analysis with the cluster_hits() function (example report).

• Run ORA with clustered hits:

  Perform over-representation analysis (ORA) using the clustered hits with the run_ora() function (example report).

🔧 Installation

Follow the steps below to install the SplineOmics package from the GitHub repository into your R environment.

Note Carefully read the terminal messages of the installations. It can happen that installations fail due to missing dependencies, which you then must resolve using other commands not necessarily written down here.

Prerequisites

• Ensure R is installed on your system. If not, download and install it from CRAN.
• RStudio is recommended for a more user-friendly experience with R. Download and install RStudio from posit.co.

Installation Steps

Note for Windows Users:

During the installation on Windows, you might see a message indicating that Rtools is not installed, which is typically required for compiling R packages from source. However, for this installation, Rtools is not necessary, and you can safely ignore this message.

1. Open RStudio or your R console in a new or existing project folder.

2. Create a virtual environment with renv

renv::init()

3. Install BiocManager for Bioconductor dependencies (if not already installed)

install.packages("BiocManager")

4. Install required Bioconductor packages

BiocManager::install(
  c("ComplexHeatmap", "limma", "variancePartition")
  # force = TRUE   # when encountering issues
)

5. Install remotes for GitHub package installation

install.packages("remotes")

5. Install the SplineOmics package from GitHub and all its non-Bioconductor dependencies, using remotes

remotes::install_github(
  "csbg/SplineOmics",   # GitHub repository
  ref = "v0.3.1",       # Specify the tag to install, e.g. v0.3.1 (check GitHub for the newest version!)
  dependencies = TRUE,  # Install all dependencies
  upgrade = "always"    # Always upgrade dependencies
  # force = TRUE        # when encountering issues
)

6. Verify the installation of the SplineOmics package

# Verify the installation of the SplineOmics package
if ("SplineOmics" %in% rownames(installed.packages())) {
  message("SplineOmics was installed successfully.")
} else {
  message("SplineOmics installation failed. Please check for errors during installation.")
}

📌 Note on documentation:
The website only contains the documentation for the most recent SplineOmics version (also shown on the website in the top right corner). To get the documentation of older versions, run:

help(package="SplineOmics")

to get the documentation of your currently installed version.

Troubleshooting

If you encounter errors related to dependencies or package versions during installation, try updating your R and RStudio to the latest versions and repeat the installation steps.

For issues specifically related to the SplineOmics package, check the Issues section of the GitHub repository for similar problems or to post a new issue.

🐳 Docker Container

Alternatively, you can run your analysis in a Docker container. The underlying Docker image encapsulates the SplineOmics package together with the necessary environment and dependencies. This ensures higher levels of reproducibility because the analysis is carried out in a consistent environment, independent of the operating system and its custom configurations.

Please note that you must have the Docker Engine installed on your machine. For instructions on how to install it, consult the official Docker Engine installation guide.

More information about Docker containers can be found on the official Docker page.

For instructions on downloading the image of the SplineOmics package and running the container, please refer to the Docker instructions.

Troubleshooting

If you face “permission denied” issues on Linux distributions, check this vignette.

▶️ Usage

🎓 Tutorial

This tutorial covers a real CHO cell time-series proteomics example from start to end.

📋 Details

A detailed description of all arguments and outputs of all the functions in the package (exported and internal functions) can be found here.

Design limma design formula

A quick guide on how to design a limma design formula can be found here.

An explanation of the three different limma results is here.

🧬 RNA-seq and Glycan Data

RNA-seq data

Transcriptomics data must be preprocessed for limma. You need to provide an appropriate object, such as a voom object, in the rna_seq_data argument of the SplineOmics object (see documentation). Along with this, the normalized matrix (e.g., the $E slot of the voom object) must be passed to the data argument. This allows flexibility in preprocessing; you can use any method you prefer as long as the final object and matrix are compatible with limma. One way to preprocess your RNA-seq data is by using the preprocess_rna_seq_data() function included in the SplineOmics package (see documentation).

Here you can find an example analysis of RNA-seq data with the SplineOmics package.

Glycan fractional abundance data

The glycan fractional abundance data matrix, where each row represents a type of glycan and the columns correspond to timepoints, must be transformed before analysis. This preprocessing step is essential due to the compositional nature of the data. In compositional data, an increase in the abundance of one component (glycan) necessarily results in a decrease in others, introducing a dependency among the variables that can bias the analysis. One way to address this issue is by applying the Centered Log Ratio (CLR) transformation to the data with the clr function from the compositions package:

library(compositions)
clr_transformed_data <- clr(data_matrix)  # use as SplineOmics input

The results from clr transformed data can be harder to understand and interpret however. If you prefer ease of interpretation and are fine that the results contain some artifacts due to the compositional nature of the data, log2 transform your data instead and use that as input for the SplineOmics package.

log2_transformed_data <- log2(data_matrix)  # use as SplineOmics input

R Version

Depending on the version of SplineOmics, for the most recent, 4.5.0 or higher.

❓ Getting Help

If you encounter a bug or have a suggestion for improving the SplineOmics package, we encourage you to open an issue on our GitHub repository. Before opening a new issue, please check to see if your question or bug has already been reported by another user. This helps avoid duplicate reports and ensures that we can address problems efficiently.

For more detailed questions, discussions, or contributions regarding the package’s use and development, please refer to the GitHub Discussions page for SplineOmics.

💬 Feedback

We appreciate your feedback! Besides raising issues, you can provide feedback in the following ways:

• Direct Email: Send your feedback directly to Thomas Rauter.

• Anonymous Feedback: Use this Google Form to provide anonymous feedback by answering questions.

Your feedback helps us improve the project and address any issues you may encounter.

📜 License

This package is licensed under the MIT License: LICENSE

© 2024 Thomas Rauter. All rights reserved.

🎓 Citation

The SplineOmics package is currently not published in a peer-reviewed scientific journal or similar outlet. However, if this package helped you in your work, consider citing this GitHub repository.

To cite this package, you can use the citation information provided in the CITATION.cff file.

You can also generate a citation in various formats using the CITATION.cff file by visiting the top right of this repo and clicking on the “Cite this repository” button.

Also, if you like the package, consider giving the GitHub repository a star. Your support helps us in the continued development and improvement of SplineOmics. Thank you for using our package!

🌟 Contributors

• Thomas-Rauter - 🚀 Wrote the package, developed the approach together with Veronika Schäpertöns under guidance from Nikolaus Fortelny and Wolfgang Esser-Skala.
• Nikolaus Fortelny - 🧠 Principal Investigator, provided guidance and support for the overall approach.
• Wolfgang Esser-Skala - Helped reviewing code, delivered improvement suggestions and scientific guidance to develop the approach.
• Veronika Schäpertöns - Developed one internal plotting function, as well as some code for the exploratory data analysis plots, and the overall approach together with Thomas Rauter.
• Dominik Hofreither - was (i) actively involved as a tester, (ii) provided datasets that helped define the problem addressed by the package, (iii) contributed numerous ideas that shaped its development, and (iv) as an end-user, provided feedback that improved its functionality.
• Larissa Hofer - contributed through feedback and beta-testing, and by providing the epigenetics and RNA datasets used as examples in the package documentation. These datasets were also used to define the problem addressed by the package.

🙏 Acknowledgements

This work was carried out in the context of the DigiTherapeutX project, which was funded by the Austrian Science Fund (FWF). The research was conducted under the supervision of Prof. Nikolaus Fortelny, who leads the Computational Systems Biology working group at the Paris Lodron University of Salzburg, Austria. You can find more information about Prof. Fortelny’s research group here.

⭐ Liking this project

If you find this package useful for your research or analysis, please consider giving the repository a star on GitHub. Starring helps increase the visibility of the project and supports its continued development.

To star the repository, you need to be logged in to your GitHub account and click the “Star” button in the top-right corner of this page.

Thank you for your support!