From data to discovery: A guided approach to proteomics analysis

Speakers:

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

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Speakers:

Eshita Mutt,
PhD

David Astling,
PhD

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

Date: June 2, 2026 | Time: 11:00 AM ET
In high‑plex proteomics, the challenge is not generating data but distinguishing true biological signal from technical variability. This webinar introduces univariate analysis as a practical starting point for biomarker discovery, covering key steps such as understanding dataset structure, identifying outliers, and assessing data performance. Using harmonized datasets, we will show how univariate methods support the identification of differentially abundant proteins and establish a reliable foundation for downstream analyses.

• Recognize sources of technical variability that obscure biological signal in high‑plex proteomics
• Understand how univariate analysis supports initial QC, outlier detection, and dataset assessment.
• See how harmonized data enables identification of differentially abundant proteins.
• Establish a reliable foundation for downstream biomarker discovery and follow‑up analyses.

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Speakers:

Julián Candia,
PhD

Mailie Gall,
PhD

Amanda Hiser,
MS

Beyond the trees: See the bigger biological picture with pathway enrichment analysis

Date: July 14, 2026 | Time: 11:00 AM ET
Proteomic data holds enormous potential—but the biology is rarely contained in a single protein. When you’re handed a list of differentially abundant proteins, the next challenge is turning that list into a clear, testable story about mechanisms and processes. In this webinar, we’ll show how pathway enrichment analysis translates protein-level results into pathway-level insight—helping you “see the forest for the trees.” You’ll learn core enrichment concepts, how to choose an approach that fits your study design, and how to interpret results to prioritize biology for downstream research. This session will feature Julián Candia, staff scientist at the National Institutes of Health, sharing practical perspective on applying enrichment analysis to real proteomics studies. For SomaScan™️ Assay users, we’ll also demonstrate the SomaEnrich analysis tool and how it can be integrated into common tertiary analysis workflows.

By the end of this session, you will be able to:

• Explain what pathway enrichment analysis is and why it helps you move beyond protein lists
• Compare common enrichment approaches, including strengths, limitations, and when to use each
• Choose practical ways to run pathway analysis that match your team’s skill set (from point-and-click tools to code-based workflows)
• Apply interpretation best practices to identify pathway-level themes that support mechanism and hypothesis generation
• See an end-to-end example of turning enrichment results into a stronger biological narrative for proteomics studies

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Speakers:

Erin Hales,
PhD

Yehonatan Elon,
PhD

Opening the black box: Building and evaluating machine learning models for proteomics

Machine learning can be a powerful way to extract multivariate signals from high-dimensional proteomics data, but only when models are built and evaluated with rigor. In this on-demand webinar, explore a practical framework for developing predictive and explanatory models, with an emphasis on study design choices that help prevent data leakage, reduce overfitting, and improve reproducibility.
Learn how to structure training and test splits, use resampling approaches such as cross-validation, and select features in ways that account for correlation, batch structure, and common confounders. The session also covers how to choose and interpret performance metrics for regression and classification, including AUC, RMSE, sensitivity, and specificity, as well as how to sanity-check models so results hold up on new samples, not just the dataset used for training.

By the end of this session, you will be able to:

• Define the goal of a multivariable model in proteomics (prediction vs. explanation) and select an appropriate modeling strategy.
• Design robust validation plans (train/test splits and cross-validation) that minimize data leakage and overfitting.
• Apply feature selection approaches that account for high dimensionality, correlation, batch structure, and common technical confounders.
• Choose and compare commonly used algorithms for regression and classification, and evaluate performance with fit-for-purpose metrics.
• Interpret and communicate model results transparently so findings can be reproduced and trusted on new data.

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Designing for discovery: Study design and statistical power in high‑dimensional proteomics

Date: November 10, 2026 | Time: 11:00 AM ET
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