High-dimensional flow cytometry has dramatically transformed our ability to explore the complexity of the immune system, allowing researchers to uncover subtle differences between cell populations that were previously hidden. Among the most widely used tools for visualizing these multi-parameter datasets are UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-distributed Stochastic Neighbor Embedding). These algorithms offer intuitive, visually appealing maps of cellular heterogeneity, but despite their popularity, very few discussions confront a crucial question: when do these methods distort biology rather than reveal it?
UMAP and t-SNE are not neutral observers. They can introduce artifacts that mislead even experienced cytometrists. Rare populations may appear as well-defined clusters when in reality they are continuous with neighboring cells, and continuous populations can be fragmented into seemingly distinct subsets. Small variations in fluorescence intensity can be exaggerated, giving the illusion of new subpopulations that do not exist biologically. In high-dimensional datasets, these distortions become particularly pronounced, and they can lead to false conclusions if embeddings are interpreted without careful validation.
The reliability of these techniques is highly dependent on preprocessing and downsampling strategies. Choices around compensation, data transformation, normalization, and batch correction have a direct impact on the resulting visualizations. Downsampling, often necessary for large cytometry datasets, can determine whether subtle but biologically meaningful populations remain visible. Otherwise, these populations may be lost entirely. This sensitivity highlights that embeddings should never be interpreted in isolation from the underlying data. Visualization outputs are only as trustworthy as the processing steps that precede them.
A particularly insidious challenge arises when UMAP or t-SNE appear to reveal new populations that are not truly present. UMAP may split a uniform population into multiple clusters to preserve local relationships. t-SNE may create gaps between phenotypically similar cells. Without careful cross-checking against original marker expression and classical gating, these apparent clusters can mislead researchers. They might believe they have discovered a rare or novel population. For labs working on translational immunology or clinical cytometry, such misinterpretations can have real consequences. These consequences include misdirected experimental focus or misleading conclusions in biomarker studies.
Responsible use of dimensionality reduction requires a combination of visualization and validation. Embeddings should always be interpreted alongside the original flow cytometry data. Key findings should also be confirmed using complementary methods, such as clustering algorithms or traditional gating strategies. Dimensionality reduction serves as a tool for exploration, rather than for definitive quantification. Transparency in data preprocessing is essential to ensure reproducibility and to allow comparability across laboratories.
By openly discussing these limitations, Immunostep positions itself as a critical voice in cytometry, emphasizing that visualizations alone cannot substitute for expert analysis. UMAP and t-SNE remain indispensable for exploring cellular complexity, but their outputs must be interpreted with caution. Artifacts, preprocessing sensitivity, and artificial population splitting all highlight the necessity of combining these algorithms with rigorous analytical practices. When used judiciously, they can illuminate biological insights without distorting the truth.
Ultimately, acknowledging the constraints of UMAP and t-SNE is not a limitation but a strength. Researchers who recognize these pitfalls can generate credible, reproducible, and meaningful insights from high-dimensional cytometry data, maintaining scientific rigor while leveraging the power of modern visualization tools.