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Updated: Apr 15, 2026

Lipidomics and Transcriptomics in Neurological Diseases

Lipidomics and Transcriptomics in Neurological Diseases

Published on: March 18, 2022

Differential network analysis with multiply imputed lipidomic data.

Maiju Kujala¹, Jaakko Nevalainen², Winfried März³

¹Department of Mathematics and Statistics, University of Turku, Turku, Finland.

|March 31, 2015

Summary

This summary is machine-generated.

Lipidomics analysis reveals complex lipid interactions in cardiovascular disease (CVD). This study introduces a novel method to analyze lipid networks, identifying key differences between patients with fatal CVD events and those who remained stable.

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Last Updated: Apr 15, 2026

Lipidomics and Transcriptomics in Neurological Diseases

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Published on: March 18, 2022

Shotgun Lipidomics of Rodent Tissues

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Published on: November 18, 2022

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Published on: March 8, 2020

Area of Science:

Biochemistry
Bioinformatics
Cardiovascular Research

Background:

Disorders in cellular lipid composition are linked to atherosclerosis and cardiovascular disease (CVD).
Lipidomics studies generate large datasets with correlated variables, posing challenges for association analysis.
Differential network analysis offers a statistical approach to compare lipid network structures between biological conditions.

Purpose of the Study:

To develop and apply a statistical method for differential network analysis of lipidomic data.
To address challenges posed by left-censored missing values in lipidomics datasets.
To identify key lipid interactions and differentially expressed lipids in coronary artery disease (CAD) patients with different prognoses.

Main Methods:

Utilized partial least square regression with multiple imputation for lipidomic data from the Ludwigshafen Risk and Cardiovascular Health (LURIC) study.
Implemented permutation testing on association scores to assess network differences.
Developed customized network analysis to handle left-censored missing values in lipidomics data.

Main Results:

Successfully applied differential network analysis to lipidomics data from the LURIC study.
Identified specific lipids and lipid classes with significant interactions.
Recognized important differentially expressed lipids distinguishing between fatal CVD events and stable CAD patients.

Conclusions:

The proposed method effectively analyzes complex lipidomic data, even with missing values.
Differential network analysis can elucidate underlying biological processes in cardiovascular disease.
This approach aids in identifying potential biomarkers for CVD risk stratification.