Finding the signal: Identifying reproducible biomarkers in high-plex proteomics

What can I do with my proteomics data?

In biomarker discovery, the challenge is rarely a lack of data. Rather, it is knowing how to separate meaningful biological signal from technical distraction. This webinar focuses on how to use univariate analysis as a practical and powerful entry point for biomarker discovery in high-plex proteomics studies.

We will cover the essentials that support trustworthy downstream discovery—from understanding dataset structure and identifying outliers to assessing dataset performance. Using harmonized data as a starting point, we will demonstrate how univariate methods help identify differentially abundant proteins, prioritize biomarker candidates, and build a strong foundation for follow-up analyses.

You will gain a practical toolkit for reproducible analysis, including programmatic resources (R and Python), web-based analytic tools, and best-practice workflows, along with a live demo of the Data Delve Statistics tool. Through real-world case studies across multiple fields, we will show how these approaches are applied in biomarker discovery to turn complex, high-plex proteomics data into actionable biological insights.

This seminar is designed to show you how to move from data delivery to confident discovery. You will learn how to find real signal, identify reproducible biomarkers, and preserve the performance integrity of your dataset.

In this webinar you’ll learn:

• Assess whether a high-plex proteomics dataset is analysis-ready by understanding expected deliverables and quality indicators.
• Confidently navigate high-plex proteomics data structures, including protein measurements, annotations, metadata, and QC flags.
• Perform rapid first-line QC checks to identify outliers, missingness patterns, batch effects, and technical performance issues.
• Apply univariate methods to identify differentially abundant proteins and prioritize reproducible biomarker candidates.
• Assemble a practical toolkit for reproducible univariate analysis, spanning R/Python resources, intuitive analytic tools, and reporting best practices.
• See how univariate analysis is applied in real-world biomarker discovery through case studies and a live DataDelve Statistics demo.

David Astling, PhD

David Astling is a service and support manager at Illumina. His team provides global scientific support, bioinformatics, and data consulting for SomaLogic customers— specializing in study design, data analysis, and extracting value from data. David has a PhD in Molecular and Cell Biology from the University of California at Berkeley. Prior to joining SomaLogic, David was a research associate in the bioinformatics core lab at the University of Colorado School of Medicine, collaborating on multiple next-generation sequencing projects.

Eshita Mutt, PhD

Eshita Mutt is a Bioinformatics Support Scientist at Illumina, where she supports researchers with high-plex proteomics data analysis and experimental study design, with a particular focus on the SomaScan Assay. She holds a PhD from the National Centre for Biological Sciences, India, and completed postdoctoral training at ETH Zurich (PSI), working in computational protein science. Eshita enjoys collaborating with multidisciplinary research teams to translate complex proteomics data into biological insight, particularly for biomarker discovery and translational research applications.