Improving metabolomics data comparability without long term quality controls using a post-acquisition correction...

Area of Science:

• Metabolomics
• Bioinformatics
• Data Science

Background:

• Advances in metabolomics analytical techniques generate high-quality data.
• Integrating metabolomic datasets is hindered by a lack of methods to correct analytical bias without long-term quality controls.
• This limitation impedes cross-study comparisons and the impact of metabolomics in precision biology.

Purpose of the Study:

• To develop and evaluate a post-acquisition strategy for improving metabolomics data comparability.
• To overcome the bottleneck preventing inter-comparisons across studies.
• To enhance the impact of metabolomics in precision biology through improved data interoperability.

Main Methods:

• A three-step workflow (PARSEC) involving data extraction, standardization, and feature filtering.
• Application of the PARSEC strategy to two case studies.
• Comparison of PARSEC with the locally estimated scatterplot smoothing (LOESS) method.

Main Results:

• The PARSEC strategy effectively reduced inter-group variability and improved sample distribution homogeneity.
• Data comparability was significantly enhanced in both case studies.
• Biological information masked by variability was revealed more clearly compared to the LOESS method.

Conclusions:

• The PARSEC strategy, combining batch-wise standardization and mixed modeling, enhances data comparability and scalability in metabolomics.
• This approach minimizes analytical condition influences while preserving biological variability by addressing batch and group effects.
• PARSEC provides a valuable tool for harmonizing datasets across studies or cohorts lacking common quality control samples.