CYTO 2025 Tutorials: Before you click ‘Run’: what truly matters for successful high-dimensional data analysis?

Before you click ‘Run’: what truly matters for successful high-dimensional data analysis? 

Anna Belkina, MD, PhD - Boston University Chobanian & Avedisian School of Medicine
Sarah Bonte, PhD - Ghent University
Givanna Putri, PhD - Walter and Eliza Hall Institute of Medical Research

When working with high-dimensional cytometry data, the instinct is often to dive straight into clustering and dimensionality reduction — but is that the best approach? Often, datasets are simply fed into automated pipelines for visualization and pattern discovery without careful consideration of how experimental design, data quality, preprocessing, and technical variations shape the results. Beyond just choosing an algorithm, in this tutorial, we will discuss how to structure a robust workflow, critically interpret outputs, and distinguish meaningful patterns from artifacts. We will also explore how automated methods can introduce bias and lead to misleading conclusions, as well as highlight a few flawed approaches that are common but can compromise analysis. Through practical examples and shared experiences, we aim to provide a framework for making informed decisions in high-dimensional data analysis.

CMLE Credit: 1.0