Biomarkers are molecules whose expression reflects biology. In an ideal world, every biological parameter could be captured by a single biomarker. We do not live in that world.

 

The connections between biomarkers and biological processes and systems are complex and so almost all biomarkers imperfectly reflect biology. For more subtle biological differences, the less information content of its associated individual biomarkers.

 

The answer is multivariate analysis. Information content from multiple biomarkers can be combined to increase confidence in the reflected connected biology. It is critical that combined biomarkers each contribute distinct information content about the system, as combining the same information content from multiple biomarkers does not increase total information.

 

Underlying these dynamics is biomarker scarcity. Biomarkers with high information content are rare. The ability to find biomarkers is directly correlated to the number of candidates considered. Larger candidate pools (greater data scale) will turn over more relevant biomarkers with unique information content.

 

This is why data scale matters. Data scale constrains the ability to find enough high information content biomarkers to distinguish between samples sets with different biology. Systems that rely on lower numbers of data points are fundamentally limited in their power.

 

Magellan’s software allows for the unbiased and rapid analysis of mass spec experiments that generate millions of data points per sample. This is the largest data scale in the industry, meaning it is poised to distinguish molecular details underpinning the most subtle biological differences.