Superior batch alignment and hyper-dimensional cytometry representations allow ultra-sensitive classification of disease phenotypes
Benjamin S. Mashford, Timothy Hewitt, Maryam May, Zixin Zhuang, Akshat Jain, Koula E. M. Diamand, Fei-Ju Li, Kristy Kwong, Stuart H. Read, Ainsley R. Davies, Dillon Hammill, T. Daniel Andrews
This paper introduces a neural network-based batch alignment method for cytometry data that preserves biological signals and enables hyperdimensional representation for improved disease phenotype classification.
bioRxiv · Jul 28, 2025 · PreprintSystems Biology