Digitizing omics profiles by divergence from a baseline.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Omics technologies reveal significant molecular heterogeneity between individual samples, even within the same disease subtype.
• Existing bioinformatics methods, like differential gene expression analysis, are population-based and fail to capture this inter-sample diversity.
• This limitation hinders personalized medicine and a deep understanding of biological variation.

Purpose of the Study:

• To develop a unified theory for quantifying sample-level heterogeneity in omics data.
• To enable the analysis of molecular dysregulation at the individual sample level.
• To facilitate personalized and biologically interpretable analyses of variation in omics profiles.

Main Methods:

• A unified theory is proposed to simplify omics profiles into a digital representation based on a reference population.
• The concept of "divergence" is introduced to identify molecular states outside the baseline distribution, indicating dysregulation.
• Analysis is performed on single features (e.g., genes) and subsets (e.g., pathways).

Main Results:

• The new theory allows for the quantification of heterogeneity at the individual omics profile level.
• Dysregulation can be assessed probabilistically, estimating the likelihood of a gene or pathway being divergent.
• The method reduces complexity, enabling more personalized and interpretable biological variation analysis.

Conclusions:

• This approach provides a powerful tool for analyzing individual sample heterogeneity in omics data.
• It supports personalized medicine by enabling a more nuanced understanding of disease states.
• Applications include tissue characterization, disease detection, progression monitoring, and pathway association studies.