Gaining Biological Insights through Supervised Data Visualization

Rhodes, J. S.; Aumon, A.; Morin, S.; Girard, M.; Larochelle, C.; Lahav, B.; Brunet-Ratnasingham, E.; Zhang, W.; Cutler, A.; Zhou, A.; Kaufmann, D. E.; Zandee, S.; Prat, A.; Wolf, G.; Moon, K. R.

doi:10.1101/2023.11.22.568384

bioRxiv · 10.1101/2023.11.22.568384

Gaining Biological Insights through Supervised Data Visualization

Rhodes, J. S.·Aumon, A.·Morin, S.·Girard, M.·Larochelle, C.·Lahav, B.·Brunet-Ratnasingham, E.·Zhang, W.·Cutler, A.·Zhou, A.·Kaufmann, D. E.·Zandee, S.·Prat, A.·Wolf, G.·Moon, K. R.

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Abstract

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATEs prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

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Rhodes, J. S., Aumon, A., Morin, S., Girard, M., Larochelle, C., Lahav, B., Brunet-Ratnasingham, E., Zhang, W., Cutler, A., Zhou, A., Kaufmann, D. E., Zandee, S., Prat, A., Wolf, G., Moon, K. R.. 2023-11-23. Gaining Biological Insights through Supervised Data Visualization. https://doi.org/10.1101/2023.11.22.568384

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