GLARE Archives

Overall pipeline of GLARE: Gene LAb Representation learning pipelinE. (a) Illustration of GLARE, starting with a verification study followed by preprocessing through detecting outliers using k-means clustering. Using the clean dataset, GLARE provides options for representation learning from PCA to state-of-the-art SAE pre-trained with high-throughput single-cell data. Retrieved data representation is then processed through ensemble clustering to find the hidden patterns within the data. Results from the verification study and ensemble clustering are then used for post-pipeline analysis. (b) Model architecture illustration of employed SAE for both training with and without pre-training. (c) Ensemble clustering using three base clustering algorithms based on different statistical methodologies. Evidence accumulation clustering is used to derive consensus clusters from these algorithms. -- biorxiv.org

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GLARE: Discovering Hidden Patterns In Spaceflight Transcriptome Using Representation Learning

by Keith CowingJune 9, 2024June 9, 2024

Spaceflight studies present novel insights into biological processes through exposure to stressors outside the evolutionary path of terrestrial organisms. Despite limited access to space environments, numerous transcriptomic datasets from spaceflight […]