Gaining Biological Insights through Supervised Data Visualization

Author:

Rhodes Jake S.ORCID,Aumon Adrien,Morin Sacha,Girard Marc,Larochelle Catherine,Brunet-Ratnasingham Elsa,Pagliuzza Amélie,Marchitto Lorie,Zhang Wei,Cutler Adele,Grand’Maison Francois,Zhou Anhong,Finzi Andrés,Chomont NicolasORCID,Kaufmann Daniel E.,Zandee StephanieORCID,Prat Alexandre,Wolf GuyORCID,Moon Kevin R.

Abstract

AbstractDimensionality 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-PHATE’s 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.

Publisher

Cold Spring Harbor Laboratory

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