From data to discovery: A guided approach to proteomics analysis
Proteomic data holds enormous potential—but only when it’s prepared and analyzed in the right way. This webinar series is designed to provide a clear, practical roadmap for turning proteomic data into meaningful biological insight.
Across six sessions, we’ll walk through the essential steps: preparing data through harmonization, understanding what questions proteomics can answer, and applying common downstream approaches including univariate analysis, pathway analysis, machine learning, multiomics integration, and study design.
Whether you’re new to proteomics or looking to make better use of existing data, this series will deliver a clear, practical understanding of how proteomics can fit into your research—and what steps to take to move forward with confidence.
David Astling, PhD
Tala Khosroheidari, PhD
More than the sum of its parts: How harmonized proteomic data reveals meaning across disparate clinical cohorts
In high‑throughput proteomics, data generated across different instruments, workflows, and cohorts can remain difficult to compare even after normalization. Harmonization addresses these study‑specific differences, enabling datasets to align and reveal meaningful biological signals. The Global Neurodegeneration Proteomics Consortium (GNPC) illustrates this power by integrating 40,000 samples from 20 international groups to uncover insights not visible in isolated studies. In this webinar, you’ll learn when normalization falls short and how harmonization strengthens cross‑study and longitudinal analyses.
Learning objectives:
- Recognize when basic normalization is insufficient for comparing across studies
- Understand how harmonization aligns datasets across plates, batches, studies and time
- See how aligned datasets reveal biological signals that remain hidden after normalization alone
- Decide when harmonization strengthens longitudinal and multi-cohort analyses –and when to use alternative approaches
Eshita Mutt, PhD
Will Schwarzmann
What can I do with SomaScan 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 seminar 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.
By the end of this session, you will be able to:
- 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.