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Updated: Jun 4, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Computational analysis workflows for Omics data interpretation.

Irmgard Mühlberger¹, Julia Wilflingseder, Andreas Bernthaler

¹Emergentec Biodevelopment GmbH, Vienna, Austria.

Methods in Molecular Biology (Clifton, N.J.)

|March 4, 2011

Summary

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This chapter details a transcriptomics data analysis workflow using open-source tools. It covers data preprocessing, normalization, and interpretation for gene expression datasets like familial hypercholesterolemia.

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Rapid advancements in experimental techniques yield abundant Omics data.
Numerous commercial and open-access tools exist for Omics data management and analysis.
Standard Omics analysis involves data handling, normalization, differential expression analysis, and pathway interpretation.

Purpose of the Study:

To present a sequential workflow for transcriptomics data analysis.
To demonstrate the application of open-source tools for analyzing gene expression data.
To illustrate the process using a familial hypercholesterolemia dataset.

Main Methods:

Utilizing open-source bioinformatics tools for data analysis.
Implementing a sequential workflow for transcriptomics data.

Related Experiment Videos

Last Updated: Jun 4, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Applying the workflow to a familial hypercholesterolemia gene expression dataset.

Main Results:

A comprehensive transcriptomics data analysis workflow is presented.
The workflow effectively processes and interprets gene expression data.
The analysis highlights key features in the familial hypercholesterolemia dataset.

Conclusions:

Open-source tools provide a viable and effective approach for transcriptomics data analysis.
The presented workflow facilitates the interpretation of complex gene expression data.
This methodology aids in understanding genetic conditions like familial hypercholesterolemia.