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Enhancing Interpretability of Dimension-Reduced Scatter Plots with Class and Feature Centroids

Core Concepts
Overlaying class and feature centroids onto two-dimensional scatter plots derived from high-dimensional biomedical datasets enhances their interpretability.
The authors present a method to enhance the interpretability of two-dimensional scatter plots obtained after dimension reduction of high-dimensional biomedical data. They demonstrate the approach using phenotype data from three rare neurogenetic diseases: hereditary spastic paraparesis (HSP), hereditary cerebellar ataxia (CA), and Charcot-Marie-Tooth disease (CMT). Key highlights: The original dataset had 235 disease variants described by 970 phenotype terms from the Human Phenotype Ontology (HPO). The phenotype terms were reduced to 31 categories using subsumption, and the data was then reduced to two dimensions using t-SNE. The x and y coordinates from t-SNE were used to calculate class centroids for the three disease types and feature centroids for the 31 phenotype categories. Overlaying these centroids onto the scatter plot provides additional context and improves the interpretability of the dimension-reduced visualization. The proximity of class and feature centroids helps interpret the relationships between disease types and their associated phenotypes. The authors discuss the limitations of their approach, including the challenge of interpreting distances between centroids due to the nonlinear nature of dimension reduction, and the need for further work on scalability and generalizability to other data types.
The original dataset had 235 rows (disease variants) and 970 columns (phenotype features). After subsumption, the dataset was reduced to 235 rows and 31 columns of phenotype features.
"Dimension reduction is a powerful tool for gaining insight into high-dimension data. As Rudin et al. [14] commented, even in data science, a picture is worth a thousand words." "The overlay of feature and class centroids onto dimension-reduced scatter plots offers new avenues for interpretation."

Deeper Inquiries

How can the interpretability of centroid-enhanced scatter plots be further improved, such as through interactive visualizations or the incorporation of additional contextual information?

To further enhance the interpretability of centroid-enhanced scatter plots, interactive visualizations can play a crucial role. By allowing users to interact with the plot, they can explore the data more dynamically. For instance, incorporating tooltips that display detailed information about specific data points when hovered over can provide additional context. Interactive features like zooming, panning, and filtering can help users focus on specific areas of interest within the plot. Additionally, incorporating linked views where changes in one visualization are reflected in others can provide a more comprehensive understanding of the data. Furthermore, the incorporation of additional contextual information can improve interpretability. This can include adding annotations, color-coding based on different criteria, or overlaying other relevant data points such as outliers or clusters. By providing more context around the centroids and data points, users can gain a deeper understanding of the relationships and patterns present in the scatter plot.

What are the potential limitations or biases that may arise from the subsumption-based reduction of phenotype features, and how can these be addressed?

One potential limitation of subsumption-based reduction of phenotype features is the loss of granularity in the data. By collapsing specific terms into more general categories, valuable information contained in the detailed descriptions may be overlooked. This can lead to oversimplification and the masking of important distinctions between different phenotypes. To address this, it is essential to carefully consider the hierarchy of the ontology and ensure that the subsumption process retains meaningful distinctions while reducing dimensionality. Biases may arise if the subsumption process disproportionately favors certain categories over others, leading to a skewed representation of the data. To mitigate this, it is crucial to validate the subsumption results and assess the impact of the reduction on the overall dataset. Sensitivity analysis and validation with domain experts can help identify and correct any biases introduced during the reduction process.

How might this approach be extended to other high-dimensional datasets beyond the biomedical domain, and what additional challenges might arise in those contexts?

The approach of enhancing scatter plots with class and feature centroids can be extended to various high-dimensional datasets beyond the biomedical domain, such as financial data, social network analysis, or image processing. However, applying this approach to different domains may present unique challenges: Data Representation: Different domains may have diverse data types (e.g., numerical, categorical, textual) that require specific preprocessing techniques before dimension reduction and centroid calculation. Interpretation: Understanding the significance of centroids in non-biomedical datasets may require domain-specific knowledge to interpret the relationships between classes and features accurately. Scalability: High-dimensional datasets in other domains may be significantly larger, requiring efficient algorithms and computational resources for dimension reduction and centroid calculation. Visualization: Visualizing high-dimensional data in non-biomedical contexts may involve different visualization techniques tailored to the specific characteristics of the data, posing challenges in creating meaningful and insightful scatter plots. Addressing these challenges would involve adapting the methodology to suit the characteristics of the new domain, collaborating with domain experts for data interpretation, and exploring innovative visualization strategies to enhance the interpretability of the scatter plots.