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Abstract:
This article from Nature Methods discusses the widespread use, power, and potential pitfalls of dimension reduction tools like t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) for visualizing high-dimensional biological datasets, particularly in genomics. While effective for revealing local data structures and clustering, experts highlight that these nonlinear methods can distort global relationships, create spurious clusters, and are often used without sufficient understanding or appropriate parameter tuning. The discussion was notably re-triggered by criticism of a UMAP figure in a recent Nature paper from the NIH All of Us Research Program, which was seen as inappropriately conflating genetic similarity with social constructs of race and ethnicity. Researchers advocate for thoughtful application, emphasizing the need to understand the methods' mathematical basis, tune parameters judiciously, view the output as exploratory insights rather than definitive findings, consider the scientific question guiding the visualization, and potentially use validation tools like scDEED to assess plot trustworthiness. They stress that while the math is neutral, responsible human use is paramount, urging better practices and collaboration with statisticians.
Seeing Data as t-SNE and UMAP Do: Thoughtful Use of Dimension Reduction Tools
Dimension reduction tools like t-SNE and UMAP are widely used, especially in genomics, to visualize high-dimensional datasets in lower dimensions (often 2D).
These tools excel at preserving local structure (keeping nearby points close) to highlight clusters, but can struggle with preserving global structure and distances between clusters.
Experts like Rafael Irizarry criticize the common misuse of t-SNE and UMAP plots, calling them "artistic renditions" that lack confidence measures and are analytically intractable, arguing their widespread uncritical use in genomics must cease.
PCA (Principal Component Analysis) is a linear method often used as a first step to compress dimensions and speed up t-SNE/UMAP processing. PCA optimizes for variance and can remove noise but doesn't incorporate biological knowledge.
Jingyi Jessica Li and others caution against choosing parameters to confirm existing biases and emphasize the need for justified parameter selection based on understanding what the tools do and the nature of the dataset's dimensions.
While some suggest avoiding t-SNE/UMAP plots, Dmitry Kobak and Philipp Berens argue they are valuable for exploratory data analysis, but the resulting plots are the beginning of analysis, not the definitive end or confirmation of biological findings.
A recent UMAP figure in a Nature paper from the NIH All of Us Research Program faced sharp criticism for potentially conflating genetic similarity (shown by clustering) with social constructs of race and ethnicity, leading to a planned revision and acknowledgment of the error.
Genevieve Wojcik and Aravinda Chakravarti link the criticism to increasing awareness in human genetics about the harms standard practices can cause and the need for better methods for representing diversity, aligning with recommendations from the NASEM report on using population descriptors.
Kobak and Berens highlight the importance of tuning parameters (like perplexity in t-SNE or hyperparameters in UMAP) as they significantly influence clustering shape (tighter/looser). They conceptualize these methods as being on an attraction-repulsion spectrum, tunable to emphasize local or global structure.
Researchers can explore the "entire spectrum" by adjusting parameters or using multiple dimension reduction methods to see how clustering changes, akin to using different microscope objectives, but remembering these are transformations, not direct data views.
Jingyi Jessica Li, Christy Lee, and Lucy Xia developed scDEED (single-cell dubious embedding detector), a tool to assess the trustworthiness of t-SNE/UMAP visualizations by providing a reliability score for data points, helping users make more informed conclusions.
The article concludes by emphasizing that collaboration with statisticians is crucial for good science, and that while dimension reduction methods are just mathematical tools, their responsible and thoughtful application by humans is key to avoiding misinterpretation and advancing research.