Functional modules from variable genes: Leveraging percolation to analyze noisy, high-dimensional data
Steffen Werner, W. Mathijs Rozemuller, Annabel Ebbing, Anna Alemany, Joleen J.H. Traets, Jeroen Sebastiaan van Zon, Alexander van Oudenaarden, Hendrik C. Korswagen, Greg J. Stephens, Thomas Simon Shimizu
The paper introduces a density-based clustering method using percolation theory to identify functionally related gene modules from noisy, high-dimensional RNA sequencing data across different organisms.
bioRxiv · Jun 10, 2020 · PreprintSystems Biology